To see the other types of publications on this topic, follow the link: Predictive maintenance systems.
Dissertations / Theses on the topic 'Predictive maintenance systems'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Predictive maintenance systems.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
AHMED, Umair. "DECISION-MAKING MODELS FOR PREDICTIVE MAINTENANCE SERVICE SUPPORT SYSTEMS." Doctoral thesis, Università degli Studi di Palermo, 2023. https://hdl.handle.net/10447/579250.
Full textAbstract:
Nell'era digitale, la tecnologia è in continua evoluzione, con enormi progressi nell'automazione che consentono una gestione della manutenzione più efficiente ed economica. Le tecnologie digitali stanno convergendo e avanzando insieme alle industrie, determinando progressi significativi nella gestione della manutenzione. La tradizionale strategia di manutenzione preventiva gestita dall'uomo lascia progressivamente spazio alla manutenzione predittiva, che rappresenta un’ottima opportunità per migliorare significativamente la pianificazione della manutenzione del sistema, in particolare per i sistemi più complessi e dal significativo valore monetario. Tuttavia, l’implementazione di tecniche di manutenzione predittiva si trova ad affrontare una serie di sfide sostanziali, essendo richiesti l’utilizzo di tecnologie di tracciamento moderne, lo sviluppo di solidi sistemi di raccolta dati e l'esecuzione di una varietà di procedure complesse.
Considerando il ruolo chiave della gestione della manutenzione nelle industrie, la motivazione principale di questo lavoro di ricerca consiste nell’indagare le pratiche esistenti e proporre nuove metodologie in grado di fornire implicazioni pratiche che possono essere utili nel contribuire a questo campo di studio in termini di previsione dei guasti, efficienza e ottimizzazione dei costi. Il presente lavoro di tesi è organizzato in tre capitoli, che rappresentano le principali aree di studio: 1) panoramica sulla gestione della manutenzione, 2) modelli decisionali a supporto della manutenzione predittiva, 3) trasformazione digitale nella gestione della manutenzione. Gli obiettivi di ricerca relativi ai menzionati capitoli sono: 1) studiare le attuali pratiche di manutenzione predittiva e le sue applicazioni nell'industria per identificare la sua capacità di prevedere e controllare i guasti delle apparecchiature di sistemi complessi; 2) studiare vari metodi di decisione multi-criterio (MCDM) e le loro applicazioni in modo da sviluppare una metodologia decisionale di manutenzione predittiva integrata per sistemi complessi nell'industria 4.0; 3) studiare la trasformazione digitale della gestione della manutenzione e i fattori critici della digitalizzazione, nonché l'incertezza nel processo decisionale per la gestione della manutenzione nell'industria 4.0.
Questi obiettivi di ricerca vengono perseguiti attraverso una metodologia mista, ovvero sia qualitativa e sia quantitativa, basata su un ampio studio della letteratura. È stata sviluppata una revisione della letteratura sulla manutenzione predittiva e le sue applicazioni industriali insieme ai suoi limiti per identificare le carenze negli approcci esistenti. Sono state inoltre studiate varie metodologie MCDM per analizzarne gli effetti nella gestione della manutenzione ed è stata sviluppata una pletora di casi reali per offrire spunti gestionali pratici.<br>In the digital era, technology is continually evolving, with enormous advancements in automation enabling more efficient and cost-effective maintenance management. Digital technologies are converging and advancing in tandem with industries, resulting in significant progress in maintenance management. The traditionally human-managed preventive maintenance strategy is outclassed with predictive maintenance, something that represents a wonderful opportunity to significantly improve system maintenance planning, particularly for more complex systems with a significant monetary value. However, predictive maintenance methods face numerous substantial challenges in terms of their application, as they necessitate the use of contemporary tracking technologies, the development of robust data-gathering systems, and the execution of a variety of intricate procedures.
Considering the significance of maintenance management in industries, the primary motivation for this research work is to investigate existing practices and propose new methodologies capable of providing practical implications that may be useful in contributing to this field of study in terms of predicting failures, efficiency, and cost optimization. The present work is organized through three chapters, representing the main areas of study: 1) overview on maintenance management, 2) decision-making models supporting predictive maintenance, and 3) digital transformation in maintenance management. The objectives of research linked to the defined chapters are; 1) to study current practices of predictive maintenance and its applications in industry to identify its capability to predict and control equipment failures of complex systems; 2) to investigate various Multi-Criteria Decision-Making (MCDM) methods and their applications so as to develop an integrated predictive maintenance decision-making methodology for complex systems in industry 4.0; 3) to study the digital transformation of maintenance management and critical factors of digitalization, as well as uncertainty in the decision-making process for maintenance management in industry 4.0.
In achieving the objectives of this research, a mixed methodology, i.e., qualitative and quantitative research, is carried out on the basis of an extensive literature study. A literature review of predictive maintenance, its industrial applications along with its limitations is developed to identify the shortcomings in existing approaches. Various MCDM methodologies have been studied as well to investigate their effects on maintenance management and a plethora of real-world cases have been developed to offer practical managerial insights.
2
Dinh, Duc-Hanh. "Opportunistic Predictive Maintenance for Multi-Component Systems with Multiple Dependences." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0171.
Full textAbstract:
Les dépendances (économiques, stochastiques et/ou structurelles) entre composants influencent de manière significative le processus de dégradation des composants ainsi que le processus de prise de décision en maintenance. En ce sens, la non prise en compte des dépendances entre composants dans la modélisation de la maintenance pourrait entraîner des surcoûts de maintenance et un planning de maintenance sous-optimal. En lien avec ces considérations de nombreux travaux en maintenance prédictive de systèmes multi-composants avec des dépendances entre composants ont été récemment faits. Cependant, la plupart des modèles de maintenance prédictive existants ne permettent de prendre en compte qu'un seul type de dépendances, car la considération de plusieurs dépendances entraîne une complexité plus importante lors de la modélisation de la dégradation mais aussi la formalisation des processus de décision et d’optimisation de la maintenance. Cependant, dans les cas réels de systèmes industriels, plusieurs types de dépendances peuvent exister ensemble, notamment les dépendances économiques et structurelles. Par exemple, la plupart des systèmes mécaniques sont construits sur une structure hiérarchique impliquant que la maintenance d'un composant nécessite le démontage d'autres composants. L’objectif de cette thèse est donc d’intégrer à la fois des dépendances économiques et structurelles dans le processus de modélisation de la dégradation et le processus de décision en maintenance d'un système à composants multiples dans le cadre de la maintenance prédictive. Plus précisément, cet objectif repose sur deux axes scientifiques majeurs. Le premier consiste à étudier l'impact des dépendances structurelles et économiques sur le processus de dégradation des composants et sur la structure des coûts de maintenance. Le deuxième axe de recherche a pour objet d’intégrer les impacts des dépendances économiques et structurelles dans les processus de décision et d'optimisation de la maintenance. Face à ces problématiques, dans cette thèse nous avons proposé trois contributions principales : (1)-Formalisation et proposition de modèles mathématiques permettant de modéliser les dépendances structurelles et économiques entre composants; (2)-Développement d'un modèle de dégradation considérant les impacts de la dépendance structurelle entre composants; (3)-Développement d'une politique de maintenance prédictive opportuniste adaptée permettant de prendre en considération les impacts des dépendances économiques et structurelles dans les processus de prise de décision et d'optimisation de la maintenance. Enfin, pour évaluer la faisabilité et la valeur ajoutée ainsi que les limites des modèles proposées dans un cadre d'optimisation de la maintenance, une étude numérique sur un convoyeur industriel est investiguée<br>Recently, maintenance modeling for multi-component systems with dependences (economic, stochastic, and/or structural dependences) has been extensively studied. However, most of the existing studies only consider one type of dependence since combining more than one makes the models too complicated to analyze and solve. However, in practice, several types of dependences, especially, the economic and structural dependences, may exist together in the system. To face this issue, the main objective of this thesis is to consider both economic and structural dependences in maintenance modeling and optimization for multi-component systems in framework of predictive maintenance. For this purpose, the impacts of economic and structural dependences on the maintenance cost, duration and the degradation process of the components are firstly investigated. Mathematical models for quantifying the impacts of the economic and structural dependences are then developed. Finally, a multi-level opportunistic maintenance policy is proposed to consider the impacts of these dependences between components.Due to the structural dependence between components, when a maintenance (preventive or corrective action) occurs, only few components need to be disassembled. The disassembled components are subjected to both economic and structural dependences while the non-disassembled components are subjected to only economic dependence. In that way, the proposed maintenance policy is characterized by one preventive threshold, that is used to select survival components for preventive maintenance, and two opportunistic maintenance thresholds, that are used for opportunistic maintenance. When a maintenance occurs, the first opportunistic threshold is defined to select the non-disassembled components (with only economic dependence) while the second opportunistic threshold is then developed to consider the disassembled components for opportunistic maintenance (with both economic and structural dependences). To evaluate the performance of the proposed opportunistic maintenance policy, a cost model is developed. Particle swarm optimization algorithm is then implemented to find the optimal decision variables. Finally, the proposed opportunistic maintenance policy is illustrated through a conveyor system to show its feasibility and added value in maintenance optimization framework
3
Ruiz, Cárcel Cristóbal. "Predictive condition monitoring of industrial systems for improved maintenance and operation." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9305.
Full textAbstract:
Maintenance strategies based on condition monitoring of the different machines
and devices in an industrial process can minimize downtime, increase the
safety of plant operations and help in the process of decision-taking for control
and maintenance actions in order to reduce maintenance and operating costs.
Multivariate statistical methods are widely used for process condition monitoring
in modern industrial sites due to the quantity of data available and the difficulties
of building analytical models in complex facilities.
Nevertheless, the performance of these methodologies is still far away from
being ideal, due to different issues such as process nonlinearities or varying
operational conditions. In addition application of the latest approaches
developed for process monitoring is not widely extended in real industry.
The aim of this investigation is to develop new and improve existing
methodologies for predictive condition monitoring through the use of
multivariate statistical methods. The research focuses on demonstrating the
applicability of multivariate algorithms in real complex cases, the improvement
of these methods in terms of fault detection and diagnosis by means of data
fusion and the estimation of process performance degradation caused by faults.<br>Marie Curie
4
Flint, Anthony David. "The development of predictive maintenance systems based on the Hough transform." Thesis, University of Huddersfield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307836.
Full text
5
Bansal, Dheeraj. "An advanced real-time predictive maintenance framework for large scale machine systems." Thesis, Aston University, 2005. http://publications.aston.ac.uk/12235/.
Full textAbstract:
This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. A crucial concept underpinning this project is that the motion current signature contains information relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of concept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network approach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the presence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear techniques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.
6
Faraj, Dina. "Using Machine Learning for Predictive Maintenance in Modern Ground-Based Radar Systems." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299634.
Full textAbstract:
Military systems are often part of critical operations where unplanned downtime should be avoided at all costs. Using modern machine learning algorithms it could be possible to predict when, where, and at what time a fault is likely to occur which enables time for ordering replacement parts and scheduling maintenance. This thesis is a proof of concept study for anomaly detection in monitoring data, i.e., sensor data from a ground based radar system as an initial experiment to showcase predictive maintenance. The data in this thesis was generated by a Giraffe 4A during normal operation, i.e., no anomalous data with known failures was provided. The problem setting is originally an unsupervised machine learning problem since the data is unlabeled. Speculative binary labels are introduced (start-up state and steady state) to approximate a classification accuracy. The system is functioning correctly in both phases but the monitoring data looks differently. By showing that the two phases can be distinguished, it is possible to assume that anomalous data during break down can be detected as well. Three different machine learning classifiers, i.e., two unsupervised classifiers, K-means clustering and isolation forest and one supervised classifier, logistic regression are evaluated on their ability to detect the start-up phase each time the system is turned on. The classifiers are evaluated graphically and based on their accuracy score. All three classifiers recognize a start up phase for at least four out of seven subsystems. By only analyzing their accuracy score it appears that logistic regression outperforms the other models. The collected results manifests the possibility to distinguish between start-up and steady state both in a supervised and unsupervised setting. To select the most suitable classifier, further experiments on larger data sets are necessary.<br>Militära system är ofta en del av kritiska operationer där oplanerade driftstopp bör undvikas till varje pris. Med hjälp av moderna maskininlärningsalgoritmer kan det vara möjligt att förutsäga när och var ett fel kommer att inträffa. Detta möjliggör tid för beställning av reservdelar och schemaläggning av underhåll. Denna uppsats är en konceptstudie för detektion av anomalier i övervakningsdata från ett markbaserat radarsystem som ett initialt experiment för att studera prediktivt underhåll. Datat som används i detta arbete kommer från en Saab Giraffe 4A radar under normal operativ drift, dvs. ingen avvikande data med kända brister tillhandahölls. Problemställningen är ursprungligen ett oövervakat maskininlärningsproblem eftersom datat saknar etiketter. Spekulativa binära etiketter introduceras (uppstart och stabil fas) för att uppskatta klassificeringsnoggrannhet. Systemet fungerar korrekt i båda faserna men övervakningsdatat ser annorlunda ut. Genom att visa att de två faserna kan urskiljas, kan man anta att avvikande data också går att detektera när fel uppstår. Tre olika klassificeringsmetoder dvs. två oövervakade maskininlärningmodeller, K-means klustring och isolation forest samt en övervakad modell, logistisk regression utvärderas utifrån deras förmåga att upptäcka uppstartfasen varje gång systemet slås på. Metoderna utvärderas grafiskt och baserat på deras träffsäkerhet. Alla tre metoderna känner igen en startfas för minst fyra av sju delsystem. Genom att endast analysera deras noggrannhetspoäng, överträffar logistisk regression de andra modellerna. De insamlade resultaten demonstrerar möjligheten att skilja mellan uppstartfas och stabil fas, både i en övervakad och oövervakad miljö. För att välja den bästa metoden är det nödvändigt med ytterligare experiment på större datamängder.
7
Yang, Lei. "Methodology of Prognostics Evaluation for Multiprocess Manufacturing Systems." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298043095.
Full text
8
Kaiser, Kevin Michael. "A simulation study of predictive maintenance policies and how they impact manufacturing systems." Thesis, University of Iowa, 2007. http://ir.uiowa.edu/etd/152.
Full text
9
Tan, Wui-Gee. "Maintenance programmer effectiveness : a survey of software maintainers' and maintenance managers' problems, methods and effectiveness with centrally-maintained application systems." Thesis, Queensland University of Technology, 1999.
Find full text
10
Purkayastha, Pratik. "Diagnostics and Prognostics of safety critical systems using machine learning, time and frequency domain analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17603.
Full textAbstract:
The prime focus of this thesis was to develop a robust Prognostic and Diagnostic Health Management module (PDHM), capable of detecting faults, classifying faults, fault progression tracking and estimating time to failure. Priority was to obtain as much accuracy as possible with the bare minimum amount of sensors as possible. Algorithms like k-Nearest Neighbors (k-NN), Linear and Non- Linear regression and development of rule engine to identify safe operating limits were deployed. The entire solution was developed using R (v 3.5.0). The accuracy of around 98% was obtained in diagnostics. For Prognostics, our ability to predict time to failure more accurately increases with time. Some balance must be there between learning horizon and predicting horizon in order to get good predictions with reasonable time left to hit catastrophic failure. In conclusion, the PDHM module works just as desired and makes Predictive maintenance, smart replacement and crisis prediction possible ensuring the safety and security of people on board and assets.
11
Jules, Etienne. "Machine Learning-Based Multivariate Time Series Analysis For Health Monitoring And Prognostics Of Complex Systems." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0017.
Full textAbstract:
L'enregistrement de données via des capteurs, sur les systèmes d'ingénierie donne accès à de nombreuses quantités d'intérêt (Quantity of Interest, QoI) évoluant dans le temps, éventuellement enregistrées à différents endroits de ces systèmes. Les séries temporelles ainsi captées peuvent servir à plusieurs fins : - la prévision, c'est-à-dire la prédiction de l'évolution temporelle de la QoI dans un avenir proche. Des exemples peuvent être trouvés dans les domaines de l'hydrologie/climatologie (précipitations, inondations, sécheresses), de l'énergie (vitesse du vent, production électrique ou consommation d'énergie), de l'économie et de la finance (surveillance des actifs financiers), de la médecine personnalisée, du réseau ou du trafic routier, etc. - le diagnostic et le pronostic (par exemple, le pronostic de la durée de vie utile restante des systèmes, la surveillance de l'état des structures en génie mécanique ou civil) - l'apprentissage d'un indicateur de qualité de vie non observé à partir de plusieurs autres données enregistrées interdépendantes. Le travail proposé vise à développer des approches basées sur l'apprentissage automatique et appliquées à des séries temporelles multivariées. Les problèmes mentionnés ci-dessus seront traités dans le cadre de l'apprentissage supervisé. Un accent particulier sera mis sur l'apprentissage multi-tâches en exploitant les informations de séries temporelles multiples et mutuellement liées pour améliorer la qualité de l'apprentissage. Les séries temporelles enregistrées sont souvent non stationnaires dans les problèmes réels. Nous traiterons donc la non-stationnarité en combinant l'analyse des ondelettes et l'apprentissage automatique. Les ondelettes serviront également à débruiter les séries temporelles. Plusieurs techniques seront étudiées pour apprendre les séries temporelles, en fonction de la quantité de données collectées par le système de surveillance (nombre de capteurs, pas de temps et durée, ...), parmi lesquelles les machines à vecteurs supports (SVM), les processus gaussiens et les réseaux neuronaux profonds. Dans le cas de petits ensembles de données, nous calculerons l'incertitude de prédiction des modèles formés, ce qui est d'une importance capitale dans certaines approches de prévision et de pronostic<br>The monitoring of engineering systems provides several useful quantities of interest (QoI) evolving with time, possibly recorded at different locations in theses systems. The monitored time series can serve several purposes: - forecasting, i.e. predicting the time evolution of the QoI in the near future. Examples can be found in hydrology/climatology (precipitations, floods, droughts), energy (wind speed, electric load or power consumption), economics and finance (monitoring of financial assets), personalized medicine, network or road traffic, ... - diagnosis and prognosis (e.g. prognosis of remaining useful life of systems, structural health monitoring in mechanical or civil engineering), - learning of an unobserved QoI from several other interrelated monitored data. The proposed work aims at developing data-driven approaches based on machine learning and applied to multivariate time series. The above mentioned problems will be addressed in the framework of supervised learning. A specific emphasis will be put on multi-task learning by leveraging the information of multiple and mutually related time series for an increased accuracy in predictions. Recorded time series are often found to be non-stationary in real problems. We will address non-stationarity by combining wavelet analysis and machine learning. Wavelets will also serve for denoising the recorded time series. Several techniques will be investigated to learn the recorded time series, depending on the amount of data collected by the monitoring system (number of sensors, time-step and duration, ...), among which support vector machines, Gaussian processes and deep neural networks. In the case of small datasets, we will compute the prediction uncertainty of the trained models, which is of paramount importance in some forecasting and prognosis approaches
12
Andreou, Stefanos Alexander. "Predictive models for pipe break failures and their implications on maintenance planning strategies for deteriorating water distribution systems." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/14978.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1986.<br>MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING<br>Bibliography: leaves 261-264.<br>by Stefanos Alexander Andreou.<br>Ph.D.
13
Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.
Full textAbstract:
Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiques<br>In the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
14
Gjordeni, Kejsi, and Ayca Kaya. "Digitizing the Maintenance Management Operation : Exploring the Opportunities of an Information System in a Railway Maintenance Organization." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264090.
Full textAbstract:
The phenomenon of digitization is transforming industries worldwide by introducing new valueproducing opportunities. In the railway industry, market liberalization has resulted in increased competition. To remain profitable in this new market environment, rail operators need to transform and acquire new digital capabilities and tools. By digitizing information-intensive processes with an information system, railway companies can reduce loss of operation time and reduce total maintenance costs. At the same time, the limited research exploring information systems in maintenance management has made it challenging for companies wanting to digitize. Significant attention has been devoted to the separate topics, however research overlapping the two areas of study has been inadequate. The thesis aims to contribute with knowledge to bridge this gap in literature by investigating the opportunities a maintenance organization potentially can capture with an information system and the success factors needed to succeed. By conducting the thesis in collaboration with the Swedish railway maintenance company MTR Tech AB the potential uses of an information system have been identified and assessed. Findings indicate that there are three main business opportunities to obtain from an information system: support of the troubleshooting process, better planning of reactive maintenance and enabling the performance of condition-based maintenance. At the same time, the profitability of an information system was found to be directly linked to its degree of utilization. Our findings have therefore allowed us to conclude that the business opportunity to pursue is the one that is most likely to be carried out fully and successfully in the prevailing circumstances. Lastly, the findings conclude that the success factors needed to capture the desired business opportunities are a dedicated project group, clear communication and information sharing, as well as adequate personnel.<br>Digitalisering har påverkat och transformerat företag över hela världen genom att erbjuda nya värdeproducerande möjligheter. För att bibehålla konkurrenskraft i en föränderlig omvärld måste järnvägsoperatörer transformera sina företag och förvärva nya digitala lösningar och verktyg kopplade till järnvägsteknologier. Genom att digitalisera informationsintensiva processer med hjälp av informationssystem, blir det möjligt för järnvägsföretag att minska förlust av drifttid samt minska den totala underhållskostnaden. Samtidigt har den begränsade forskningen gällande användning av informationssystem i underhållsorganisationer försvårat digitaliseringsförsöken. Litteratur och tidigare studier har behandlat de två ämnena separat, dock har överlappande forskning varit otillräcklig. Denna studie syftar till att bidra med kunskap för att överbrygga gapet i litteraturen genom att undersöka de vinningar en underhållsorganisation kan erhålla med hjälp av ett informationssystem och de framgångsfaktorer som krävs för att uppnå dem. Genom att utföra denna studie i samarbete med det svenska underhållsbolaget MTR Tech AB har de potentiella användningsområdena av ett informationssystem identifierats. De tre huvudsakliga affärsmöjligheterna som kan erhållas från ett informationssystem är: stödjande av felsökningsprocessen, bättre planering av avhjälpande underhåll, samt möjliggörandet av tillståndsbaserat underhåll. Samtidigt har det visat sig att lönsamheten av ett informationssystem är direkt kopplat till dess utnyttjandegrad. Vi har således dragit slutsatsen att den affärsmöjlighet som bör eftersträvas är den som med största sannolikhet kommer att genomföras framgångsrikt under rådande omständigheter. Slutligen visar våra resultat att de framgångsfaktorer som krävs för att uppnå affärsmöjligheterna är en dedikerad projektgrupp, tydlig kommunikation och informationsdelning, samt lämplig personal.
15
Revanur, Vandan, and Ayodeji Ayibiowu. "Automatic Generation of Descriptive Features for Predicting Vehicle Faults." Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42885.
Full textAbstract:
Predictive Maintenance (PM) has been increasingly adopted in the Automotive industry, in the recent decades along with conventional approaches such as the Preventive Maintenance and Diagnostic/Corrective Maintenance, since it provides many advantages to estimate the failure before the actual occurrence proactively, and also being adaptive to the present status of the vehicle, in turn allowing flexible maintenance schedules for efficient repair or replacing of faulty components. PM necessitates the storage and analysis of large amounts of sensor data. This requirement can be a challenge in deploying this method on-board the vehicles due to the limited storage and computational power on the hardware of the vehicle. Hence, this thesis seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. This low dimensional representation will be used for predicting vehicle faults, specifically Turbocharger related failures. Since the Logged Vehicle Data (LVD) was base on all the data utilized in this thesis, it allowed for the evaluation of large populations of trucks without requiring additional measuring devices and facilities. The gradual degradation methodology is considered for describing vehicle condition, which allows for modeling the malfunction/ failure as a continuous process rather than a discrete flip from healthy to an unhealthy state. This approach eliminates the challenge of data imbalance of healthy and unhealthy samples. Two important hypotheses are presented. Firstly, Parallel StackedClassical Autoencoders would produce better representations com-pared to individual Autoencoders. Secondly, employing Learned Em-beddings on Categorical Variables would improve the performance of the Dimensionality reduction. Based on these hypotheses, a model architecture is proposed and is developed on the LVD. The model is shown to achieve good performance, and in close standards to the previous state-of-the-art research. This thesis, finally, illustrates the potential to apply parallel stacked architectures with Learned Embeddings for the Categorical features, and a combination of feature selection and extraction for numerical features, to predict the Remaining Useful Life (RUL) of a vehicle, in the context of the Turbocharger. A performance improvement of 21.68% with respect to the Mean Absolute Error (MAE) loss with an 80.42% reduction in the size of data was observed.
16
Larsson, Olsson Christoffer, and Erik Svensson. "Early Warning Leakage Detection for Pneumatic Systems on Heavy Duty Vehicles : Evaluating Data Driven and Model Driven Approach." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261207.
Full textAbstract:
Modern Heavy Duty Vehicles consist of a multitude of components and operate in various conditions. As there is value in goods transported, there is an incentive to avoid unplanned breakdowns. For this, condition based maintenance can be applied.\newline This thesis presents a study comparing the applicability of the data-driven Consensus SelfOrganizing Models (COSMO) method and the model-driven patent series introduced by Fogelstrom, applied on the air processing system for leakage detection on Scania Heavy Duty Vehicles. The comparison of the two methods is done using the Area Under Curve value given by the Receiver Operating Characteristics curves for features in order to reach a verdict.\newline For this purpose, three criteria were investigated. First, the effects of the hyper-parameters were explored to conclude a necessary vehicle fleet size and time period required for COSMO to function. The second experiment regarded whether environmental factors impact the predictability of the method, and finally the effect on the predictability for the case of nonidentical vehicles was determined.\newline The results indicate that the number of representations ought to be at least 60, rather with a larger set of vehicles in the fleet than with a larger window size, and that the vehicles should be close to identical on a component level and be in use in comparable ambient conditions.\newline In cases where the vehicle fleet is heterogeneous, a physical model of each system is preferable as this produces more stable results compared to the COSMO method.<br>Moderna tunga fordon består av ett stort antal komponenter och används i många olika miljöer. Då värdet för tunga fordon ofta består i hur mycket gods som transporteras uppstår ett incitament till att förebygga oplanerade stopp. Detta görs med fördel med hjälp av tillståndsbaserat underhåll. Denna avhandling undersöker användbarheten av den data-drivna metoden Consensus SelfOrganizing Models (COSMO) kontra en modellbaserad patentserie för att upptäcka läckage på luftsystem i tunga fordon. Metoderna ställs mot varandra med hjälp av Area Under Curve-värdet som kommer från Receiver Operating Characteristics-kurvor från beskrivande signaler. Detta gjordes genom att utvärdera tre kriterier. Dels hur hyperparametrar influerar COSMOmetoden för att avgöra en rimlig storlek på fordonsflottan, dels huruvida omgivningsförhållanden påverkar resultatet och slutligen till vilken grad metoden påverkas av att fordonsflottan inte är identisk. Slutsatsen är att COSMO-metoden med fördel kan användas sålänge antalet representationer överstiger 60 och att fordonen inom flottan är likvärdiga och har använts inom liknande omgivningsförhållanden. Om fordonsflottan är heterogen så föredras en fysisk modell av systemet då detta ger ett mer stabilt resultat jämfört med COSMO-metoden.
17
Bin, Hasan M. M. A. "Current based condition monitoring of electromechanical systems : model-free drive system current monitoring : faults detection and diagnosis through statistical features extraction and support vector machines classification." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5732.
Full textAbstract:
A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity
18
Popescu, George. "Digital Signal Processing Methods for Safety Systems Employed in Nuclear Power Industry." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479815935917872.
Full text
19
Piretti, Andrea. "Fault Detection in Industry 4.0 with Deep Learning Approaches." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22368/.
Full textAbstract:
Con il costante aumento dell'utilizzo di macchinari automatici in ambito industriale, nasce la ricerca della creazione di sistemi in grado garantire ottime prestazioni e tolleranza ai comportamenti anomali di essi.
L'obbiettivo di questa tesi è la realizzazione di modelli di Machine Learning in grado di svolgere operazioni di Anomaly Detection per la classificazione di comportamenti sbagliati da parte di questo tipo di macchinari mediante l'utilizzo di un AutoEncoder con un approccio di Semi-Supervised learning.
Attraverso i risultati di questi modelli sarà poi possibile svolgere un'ampia analisi sulle ragioni di questi comportamenti errati e fare predizioni di essi in modo da avere una tolleranza maggiore ai guasti sulla macchina.
20
Huppert, Nathalia, and Viggo Stenholm. "Business modeling for predictive services in the process industry : A case study with a systems thinking approach." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39833.
Full textAbstract:
This study aims to identify business opportunities created by digitalization. The purpose is to evaluate how these opportunities could be utilized, by identifying areas of improvements in the service portfolio. This study is a qualitative case study with an abductive approach, mainly using semi-structured interviews to collect data. The case study was conducted at ABB Industrial Automation Services, where the new Collaborative Operations Center was studied. The data was analyzed from a systems thinking perspective, using a Rich Business Framing sessions as an evaluation platform. The results illustrate how difficult the transition towards a more digitalized industry is. This stems from how different customers have reached different levels of automation in their plants. Therefore, the service portfolio must be flexible and agile, in order to cater to the different customer segments. This case study identified three customer segments, one focusing on internal activities, while the other two consist of customers that have reached different levels of maturity within digitalization. Different value potentials were identified, leading to a proposal of a service portfolio. The systems thinking approach was then evaluated, leading to the conclusion that it is a useful tool, but a time-consuming process.<br>Denna studie går ut på att identifiera affärsmöjligheter skapade av digitalisering. Syftet är att utvärdera hur dessa möjligheter kan utnyttjas, genom att identifiera förbättringsområden i serviceportföljen. Denna studie är en kvalitativ fallstudie med en abduktiv karaktär, där huvudsakligen semistrukturerade intervjuer användes för att samla data. Fallstudien genomfördes på ABB Industrial Automation Services, där det nya Collaborative Operations Center studerades. Den samlade informationen analyserades från ett systemtänk perspektiv, där en Rich Business Framing session användes som utvärderingsverktyg. Resultatet illustrerar hur svår övergången mot en mer digitaliserad industri är. Denna svårighet har sin grund i hur olika kunder har nått olika nivåer av automationsgrad i deras anläggningar. Detta leder till att serviceportföljen måste uppfylla vissa krav, som att vara flexibel och anpassningsbar, för att nå de olika kundsegmenten. Denna fallstudie identifierade tre kundsegment, där en fokuserar på interna aktiviteter och de andra två består av kunder som kommit olika långt inom digitalisering. Olika värdepotentialer har identifierats, vilket ledde till ett förslag av en serviceportfölj. Systemtänk synsättet har sedan utvärderats, vilket resulterade i slutsatsen att det är ett användbart verktyg, även om processen är tidskrävande.
21
Aouini, Marwen. "Système intelligent utilisant les ondes ultrasonores guidées et le forage de données en vue de la maintenance prédictive." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0228.
Full textAbstract:
A l’ère de l’industrie 4.0, la maintenance prédictive d’une part et les objets-connectés d’une autre part ne cessent de gagner du terrain. Ladite maintenance n’exploite pas ou peu la surveillance de l’intégrité des structures (connue plus par SHM, l’acronyme de son appellation anglaise) par notamment les ondes ultrasonores guidées (OUG). L’objectif final de la thèse est de développer un outil permettant de renforcer ce type de maintenance. Le SHM est une approche émergente qui permet d’assurer un contrôle en continue de la santé structurelle. Elle se fait généralement en trois étapes principales : acquisition de données, détection et localisation de défaut (diagnostic) et estimation de la durée de vie résiduelle de la structure (pronostic). La première étape requiert l’utilisation des systèmes de contrôle non destructif tels que les OUG dans la présente thèse. Cependant, ces systèmes ont été destinés à réaliser du contrôle ponctuel et nécessite l’intervention d’un personnel qualifié. Dans ce travail de thèse, un système de génération et d’acquisition de données d’OUG, permettant entre-autres de connecter la structure à surveiller à un réseau cellulaire, a été développé. Ceci permet la construction de bases de données (pouvant être hétérogènes) d’une façon automatique et à bas coût. En outre, une attention particulière a été accordée à l’optimisation de son alimentation électrique afin de garantir le plus d’autonomie possible. La deuxième étape consiste à exploiter ces données afin de détecter la présence éventuelle de défaut et de le localiser. Trois approches ont été proposées en fonction notamment de la puissance de calcul nécessaire et du degrés de non-stationnarité de ces données (i.e. dues à l’instabilité de l’environnement de la structure et dudit système de mesure). Les trois approches reposent sur la technique de détection de nouveauté. Dans le cas où un défaut est détecté, les algorithmes de prédiction de l’évolution de celui-ci dans le temps peuvent être utilisés afin d’estimer la durée de vie résiduelle de la structure ce qui constitue la dernière étape de surveillance. Ici, une méthodologie basée sur un algorithme hybride, utilisant la technique de décomposition en modes empirique et un modèle autorégressif intégré à moyenne mobile, a été développée. Les résultats obtenus sur des structures placées en laboratoire et in-situ montre la pertinence de la méthodologie de surveillance proposée. D’autres travaux complémentaires sont néanmoins nécessaires afin d’améliorer la maturation technologique du système développé<br>In the Industry 4.0 era, predictive maintenance and internet-of-things are gaining ground. This kind of maintenance does not include yet structural health monitoring (SHM) by guided ultrasonic waves (UGW) in particular. The final objective of the thesis is to develop a tool to enhance this type of maintenance. SHM is an emerging approach that allows continuous monitoring of the structural health of a given structure. It is generally done in three main steps: data acquisition, defect detection and localization (diagnosis) and estimation of the residual life (prognosis). The first step requires the use of non-destructive testing systems such as that of UGW in this thesis. However, these systems were designed to perform spot checks and require the intervention of qualified operators. In this thesis, a system of generation and acquisition of UGW data, allowing among other things to connect the structure to be monitored to a cellular network, has been developed. This allows the construction of databases (which can be heterogeneous) in an automatic and low-cost way. Moreover, a particular attention was paid to the optimization of its power supply to guarantee the most autonomy possible. The second step consists in exploiting these data in order to detect the defect and to localize it. Three approaches have been proposed, depending on the required computing power and the degree of non-stationarity of the data (i.e. due to the instability of the environment of the structure and of the said measurement system). All three approaches are based on the novelty detection technique. In the case where a defect is detected, prediction algorithms of its evolution in time can be used to estimate the residual life of the structure, which is the last monitoring step. Here, a methodology based on a hybrid algorithm, using the empirical mode decomposition technique and an integrated moving average autoregressive model, has been developed. The results obtained on laboratory and in-situ structures show the relevance of the proposed monitoring methodology. Nevertheless, further work is needed to improve the technological maturation of the developed system
22
Neto, Antonio Vieira da Silva. "Modelo de predição de falhas baseado em processos estocásticos e filtragem Kalman para suporte à manutenção preditiva de sistemas elétricos, eletrônicos e programáveis." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-19032015-160659/.
Full textAbstract:
Com o aumento do uso de sistemas elétricos, eletrônicos e programáveis em aplicações de diversos domínios, tais como entretenimento, realização de transações financeiras, distribuição de energia elétrica, controle de processos industriais e sinalização e controle em transporte de passageiros e carga, é essencial que as políticas de manutenção utilizadas sejam capazes de minimizar os custos associados a eventuais falhas que afetem negativamente os serviços providos. Ao longo das últimas décadas, foi sedimentada a tendência de que a adoção de técnicas de manutenção preditiva representa uma das abordagens mais viáveis e promissoras para que falhas de sistemas utilizados em diversas aplicações possam ser detectadas antes de elas efetivamente ocorrerem. Considerando-se que uma parcela significativa dos estudos recentes na área de manutenção preditiva de sistemas apresenta como limitação o custo elevado para se instalar uma infraestrutura específica para realizar a coleta de dados que serão usados para dar suporte à predição das falhas futuras de um sistema, o modelo proposto no presente estudo visa permitir que os índices de dependabilidade e as falhas futuras de sistemas elétricos, eletrônicos e programáveis sejam estimados utilizando-se dados já disponíveis de falhas e manutenções passadas. Para tanto, foram empregadas técnicas como processos estocásticos, filtragem Kalman e modelos de incorporação de dados de histórico preconizados no padrão internacional RIAC-HDBK-217Plus. Como principal conclusão do presente trabalho, é possível ressaltar que foi possível atingir, com o modelo proposto, o objetivo de suporte à manutenção preditiva de sistemas elétricos, eletrônicos e programáveis a partir do uso de dados preexistentes de histórico operacional; no entanto, foram constatadas limitações no grau de utilização prática do modelo em situações nas quais a quantidade dos dados de histórico disponíveis para consulta é pequena.<br>With the increased use of electrical, electronic and programmable systems in various application fields such as entertainment, financial transactions, power distribution, industrial process control and signaling and control of transportation modes, it is essential for the maintenance policies used in those systems to be able to minimize the costs of any faults that may adversely affect the services provided. Over the past decades, the use of predictive maintenance techniques has shown to be a viable and promising approach to detect faults before they actually occur in systems used in different application fields. Considering that a significant part of the recent scientific research in the area of predictive maintenance usually demands high-cost infrastructure to be installed to support the acquisition of all the data that will be used to calculate the prediction of future faults of a system, the model proposed within this study was designed to allow both dependability levels and future faults of electrical, electronic and programmable systems to be estimated using past faults and maintenance data that may already be available. For this purpose, techniques such as stochastic processes, Kalman filtering and models prescribed within the international standard RIAC-HDBK-217Plus to incorporate history data to dependability calculation were used. As the main conclusion of this study, it is possible to highlight that the main objective of the model proposed, related to its ability to support predictive maintenance of electrical, electronic and programmable systems through the use of pre-existing operating history data, has been reached; nevertheless, limitation of practical use of the model was verified in situations in which not enough operating data is available.
23
Le, Thanh Trung. "Contribution to deterioration modeling and residual life estimation based on condition monitoring data." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT099/document.
Full textAbstract:
La maintenance prédictive joue un rôle important dans le maintien des systèmes de production continue car elle peut aider à réduire les interventions inutiles ainsi qu'à éviter des pannes imprévues. En effet, par rapport à la maintenance conditionnelle, la maintenance prédictive met en œuvre une étape supplémentaire, appelée le pronostic. Les opérations de maintenance sont planifiées sur la base de la prédiction des états de détérioration futurs et sur l'estimation de la vie résiduelle du système. Dans le cadre du projet européen FP7 SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment en Anglais), cette thèse se concentre sur le développement des modèles de détérioration stochastiques et sur des méthodes d'estimation de la vie résiduelle (Remaining Useful Life – RUL en anglais) associées pour les adapter aux cas d'application du projet. Plus précisément, les travaux présentés dans ce manuscrit sont divisés en deux parties principales. La première donne une étude détaillée des modèles de détérioration et des méthodes d'estimation de la RUL existant dans la littérature. En analysant leurs avantages et leurs inconvénients, une adaptation d’une approche de l'état de l'art est mise en œuvre sur des cas d'études issus du projet SUPREME et avec les données acquises à partir d’un banc d'essai développé pour le projet. Certains aspects pratiques de l’implémentation, à savoir la question de l'échange d'informations entre les partenaires du projet, sont également détaillées dans cette première partie. La deuxième partie est consacrée au développement de nouveaux modèles de détérioration et les méthodes d'estimation de la RUL qui permettent d'apporter des éléments de solutions aux problèmes de modélisation de détérioration et de prédiction de RUL soulevés dans le projet SUPREME. Plus précisément, pour surmonter le problème de la coexistence de plusieurs modes de détérioration, le concept des modèles « multi-branche » est proposé. Dans le cadre de cette thèse, deux catégories des modèles de type multi-branche sont présentées correspondant aux deux grands types de modélisation de l'état de santé des système, discret ou continu. Dans le cas discret, en se basant sur des modèles markoviens, deux modèles nommés Mb-HMM and Mb-HsMM (Multi-branch Hidden (semi-)Markov Model en anglais) sont présentés. Alors que dans le cas des états continus, les systèmes linéaires à sauts markoviens (JMLS) sont mis en œuvre. Pour chaque modèle, un cadre à deux phases est implémenté pour accomplir à la fois les tâches de diagnostic et de pronostic. A travers des simulations numériques, nous montrons que les modèles de type multi-branche peuvent donner des meilleures performances pour l'estimation de la RUL par rapport à celles obtenues par des modèles standards mais « mono-branche »<br>Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models
24
Filho, Gilberto Figueiredo Pinto. "Degradação induzida pelo potencial em módulos e instalações fotovoltaicas de c-Si." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/106/106131/tde-21122017-110248/.
Full textAbstract:
Este trabalho apresenta abordagens para a avaliação do fenômeno da Degradação Induzida pelo Potencial (PID do inglês Potential Induced Degradation) em módulos e instalações fotovoltaicas de c-Si. Nos ensaios em laboratório, a IEC TS 62804-1:2015 foi aplicada e ações adicionais são sugeridas como forma de adaptação da especificação técnica para o acompanhamento da degradação durante o ensaio e para melhor indicar a propensão do equipamento a se recuperar das consequências da aparição de PID. Nos ensaios em campo, avaliou-se a solução convencional do mercado de reverter a degradação através de circuitos anti-PID, além de apresentar a aplicação de técnicas de detecção do fenômeno em sistemas operacionais. A abordagem teórica e os resultados práticos mostram que o procedimento de aferição de tensões individuais de operação é um método útil para detectar PID. Os estudos de caso apresentados indicam que esta metodologia é eficaz inclusive na detecção precoce do fenômeno para diferentes topologias de células fotovoltaicas de c-Si.<br>This work presents approaches to assess the Potential Induced Degradation (PID) on c-Si photovoltaic modules and installations. The IEC TS 62804-1:2015 was applied to the laboratory tests and some additional actions are suggested. The adaptation of the technical specification aims to monitor the degradation rates during the tests and also to consider the capacity of the photovoltaic modules to recover from the degradation. In the field detection methodologies are presented and anti-PID circuits were also tested. The theoretical approach reveals that individual voltage measurements are useful to detect PID even in its early stage, as can be seen on the case studies presented.
25
Campi, Rodrigo Luz. "Modelagem fuzzy da concentração dos gases dissolvidos em óleo mineral isolante de transformadores baseada em resultados de ensaios físico-químicos." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-07032014-143351/.
Full textAbstract:
O objetivo desse trabalho foi de fazer a modelagem por meio de sistemas de inferência fuzzy da concentração dos gases dissolvidos em óleo mineral isolante à partir dos resultados de ensaios físico-químicos. Dessa forma, objetivou-se estender as técnicas de identificação de falhas em transformadores por meio da análise dos ensaios físico-químicos do óleo isolante. Para tanto adotou-se um mapeamento entre os dados de ensaios físico-químicos e de cromatografia gasosa feito por meio de sistemas de inferência fuzzy. Assim, por meio de resultados de ensaios físico-químicos, como cor, densidade, unidade, entre outro, tem-se uma estimativa da concentração dos gases dissolvidos no óleo mineral isolante do transformador. Assim, torna-se possível empregar técnicas de identificação de falhas baseadas na concentração dos gases dissolvidos, mas, valendo-se dos dados de ensaios físico-químicos.O sistema proposto foi validado por meio de dados reais e os resultados alcançados são compatíveis com aqueles obtidos por meio das técnicas convencionais.<br>The objective of this work was to do a modeling using the inference fuzzy system of the concentration of dissolved gases in insulating mineral oil getting from the physical-chemical results. The idea was to understand the techniques to identify failures on transformers by analyzing the physical-chemical results of the insulating mineral oil.To do that, the data from physical-chemical results and chromatographic results was mapped using the inference fuzzy system. So, by the results of the physical-chemical experiment such as color, density, humidity and so on, its possible to have a estimation of the concentration of dissolved gases in insulating mineral oil. Therefore, its possible to implement techniques to identify failures based on the concentration of dissolved gases using physical-chemical techniques.The propose system was validated by real data. The results using physical-chemical techniques were similar with the results using conventional techniques.
26
Bouaziz, Mohammed Farouk. "Contribution à la modélisation Bayésienne de l'état de santé d'un système complexe : application à l'industrie du semi-conducteur." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00993732.
Full textAbstract:
Pour maintenir leur compétitivité, les industries du semi-conducteur doivent être en mesure de produire des circuits intégrés en technologies avancées, avec des temps de cycle de plus en plus courts et à des coûts raisonnables. Un des axes d'amélioration réside dans le traitement des défaillances des équipements de production tenus responsables de plus de 50%des rejets produits. Cette thèse se fixe comme objectif de contribuer au développement d'une boucle réactive partant d'une dérive produit à la mise en place d'une solution appropriée tout en assurant un meilleur compromis entre disponibilité des équipements, coûts d'exploitation, qualité et compétitivité du produit. Joignant l'expertise humaine et les événements réels, nous nous sommes proposé ici de développer une méthodologie générique permettant de construire un modèle d'estimation du comportement des équipements de production (Equipment Health Factor EHF) à partir d'un raisonnement mathématique centré sur un formalisme probabiliste. L'approche a été amenée à sa validation expérimentale sur des outils, à base de réseaux Bayésiens, que nous avons développés. Les résultats obtenus amènent des éléments de décision permettant à l'industriel d'intervenir au plus tôt pour envisager par exemple de maintenir l'équipement avant qu'il n'ait dérivé. Cette thèse a été préparée dans le cadre du projet européen IMPROVE en collaboration avec STMicroelectronics, Lfoundry et Probayes
27
Zoubeirou, A. Mayaki Mansour. "Méthodes d'apprentissage profond pour la détection d'anomalies et de changement de régimes : application à la maintenance prédictive dans des systèmes embarqués." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4010.
Full textAbstract:
Dans le contexte de l'Industrie 4.0 et de l'Internet des Objets (IoT), la maintenance prédictive est devenue cruciale pour optimiser la performance et la durée de vie des dispositifs et équipements électroniques. Cette approche, qui repose sur une analyse extensive des données, est basée sur deux concepts essentiels : la détection d'anomalies et la détection de dérive.La détection d'anomalies est essentielle pour identifier les écarts par rapport aux normes établies, signalant des problèmes potentiels tels que les dysfonctionnements des équipements. La détection de dérive, en revanche, suit les changements dans les distributions de données au fil du temps, abordant la "dérive conceptuelle" pour maintenir la pertinence des modèles prédictifs dans des systèmes industriels en évolution. Cette thèse souligne non seulement la relation synergique entre ces techniques, mais met également en lumière leur impact collectif dans les stratégies de maintenance proactive.Nous abordons les défis de la maintenance prédictive tels que la qualité des données, l'étiquetage, les complexités des systèmes industriels, les nuances de la détection de dérive et les exigences du traitement en temps réel.Une partie importante de cette recherche se concentrera sur la manière d'adapter et d'utiliser ces techniques dans le contexte des systèmes embarqués.Les contributions de ce travail sont à la fois théoriques et appliquées, s'étendant aux économies de coûts, à la réduction de l'impact environnemental et s'alignant sur les avancées de l'Industrie 4.0. La maintenance prédictive est positionnée comme un élément clé de la nouvelle ère d'efficacité et de durabilité industrielle.Cette étude introduit de nouvelles méthodes utilisant des techniques statistiques et d'apprentissage automatique, efficaces dans diverses applications industrielles<br>In the context of Industry 4.0 and the Internet of Things (IoT), predictive maintenance has become vital for optimizing the performance and lifespan of electronic devices and equipment. This approach, reliant on extensive data analysis, stands on two pillars: anomaly detection and drift detection. Anomaly detection plays a crucial role in identifying deviations from established norms, thereby flagging potential issues such as equipment malfunctions.Drift detection, on the other hand, monitors changes in data distributions over time. It addresses "concept drift" to ensure the continued relevance of predictive models in evolving industrial systems. This thesis highlights the synergistic relationship between these two techniques, demonstrating their collective impact in proactive maintenance strategies. We address various challenges in predictive maintenance such as data quality, labeling, complexities of industrial systems, the nuances of drift detection and the demands of real-time processing. A significant part of this research will focus on how to adapt and use these techniques in the context of embedded systems. The significance of this work extends to cost savings, environmental impact reduction and aligning with the advancements in Industry 4.0, positioning predictive maintenance as a key component in the new era of industrial efficiency and sustainability.This study introduces novel methods employing statistical and machine learning techniques, validated in various industrial settings like modern manufacturing plants. These methods, both theoretical and applied, effectively address the challenges of predictive maintenance
28
Ye, Chen S. M. Massachusetts Institute of Technology. "A system approach to implementation of predictive maintenance with machine learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118502.
Full textAbstract:
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 87-91).<br>Digital technology is changing the industrial sector, yet how to make rational use of some technologies and create considerable value in a variety of industrial scenarios is an issue. Many digital industrial companies have stated that they have helped clients with their digital transformation, create much value, but the real effects have not been shown in public. Venture capitals firms have made huge investment in potential digital industrial startups. Numerous industrial IoT platforms are emerging in the market, but a number of them fade soon after. Many people have heard about industrial maintenance technology, but they have difficulty in differentiate concepts such as reactive maintenance, planned maintenance, proactive maintenance, and predictive maintenance. Many people know that big data and Al are essential in industrial sector, but they do not know how to process, analyze, and extract value from industrial data and how to use Al algorithms and tools to implement a research project. This thesis analyzes the entire digital industrial ecosystem in various dimensions such as initiatives, technologies in related domains, stakeholders, markets, and strategies. This work also analyzes of the predictive maintenance solution in various dimensions such as background, importance, suitable scenarios, market, business model, and technology. The author plans an experiment for the predictive maintenance solution, including goal, data source and description, methods and steps, and flow and tools. Then author uses a baseline approach and an optimal approach to implement the experiment, including data preparation, selection and evaluation of both regression and classification models, and deep learning practice through neural network building and optimization. Finally, contributions and expectations, and limitations and future research are discussed. This work uses a system approach, including system architecting, system engineering, and project management, to complete the process of analysis, design, and implementation.<br>by Chen Ye.<br>S.M. in Engineering and Management
29
Hallberg, Daniel. "System for Predictive Life cycle Management of Buildings and Infrastructures." Doctoral thesis, Stockholm : Skolan för arkitektur och samhällsbyggnad, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10312.
Full text
30
Vorster, Christo. "Fault diagnostic system for predictive maintenance on a Brayton cycle power plant / C. Vorster." Thesis, North-West University, 2004. http://hdl.handle.net/10394/254.
Full textAbstract:
Model-based fault detection and diagnostic systems have become an important solution (Munoz &
Sanz-Bobi, 1998:178) in the industry for preventive maintenance. This not only increases plant
safety, but also reduces down time and financial losses. This paper investigates a model-based
fault detection and diagnostic system by using neural networks.
To mimic process models, a normal feed-forward neural network with time delays is implemented
by using the MATLAB@ neural network toolbox. By using these neural network models, residuals
are generated. These residuals are then classified by using other neural networks. The main
process in question is the Brayton cycle thermal process used in the pebble bed modular reactor.
Flownet simulation software is used to generate the data, where practical data is absent.
Various training algorithms were implemented and tested during the investigation of modelling
and classification concepts on two benchmark processes. The training algorithm that performed
best was finally implemented in an integrated concept<br>Thesis (M.Ing. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2004.
31
Croker, John. "A methodology for the prediction of maintenance and support of fleets of repairable systems." Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370016.
Full text
32
Edwards, David John. "A methodology for predicting the total average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry." Thesis, University of Wolverhampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268092.
Full textAbstract:
Research into the financial management of construction plant and equipment maintenance is scant, despite the increased utilisation of mechanisation to augment productivity in recent years. This thesis addresses the shortage of meaningful research by developing a methodology for predicting the total average hourly maintenance costs of tracked hydraulic excavators operating in opencast mining. Initial pilot and field studies conducted revealed that maintenance management (in the form of record keeping and attitude to used oil analysis) within the plant hire and general construction industry was generally poor. Hence, the decision was made to focus the research upon plant operated by opencast mining contractors. Here, plant managers were found to utilise an optimum blend of predictive and fixed-time-to maintenance and also maintain a depth of machine history file data. Modelling total maintenance costs using multiple regression (MR) analysis at the five percent level of significance identified four key predictor variables. These were: machine weight; attitude to used oil analysis (regular use or not); type of industry (opencast coal or slate); and type of machine (backacter or front shovel). However, in order to determine the model's robustness an alternative modelling technique, namely artificial neural networks (ANN) was applied using the same variables identified as significant predictor variables in the regression analysis. Performance analysis conducted on the predictive power of both MR and ANN models revealed that overall the ANN model exhibited greater predictive performance. The thesis concludes with direction for future research and moreover, identifies the need for a more fastidious approach to maintenance management.
33
Darure, Tejaswinee. "Contribution à l’optimisation de la performance énergétique des bâtiments de grande dimension : une approche intégrée diagnostic / commande économique et coopérative à horizon glissant." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0142/document.
Full textAbstract:
Au cours des deux dernières décennies, la prise de conscience du changement climatique et des conséquences du réchauffement climatique a incité diverses institutions à prendre de nouvelles directives. Ces directives portent principalement sur le contrôle des émissions des gaz à effet de serre, sur l'utilisation des ressources énergétiques non conventionnelles et l'optimisation de la consommation d'énergie dans les systèmes existants. L'Union européenne a proposé de nombreux projets dans le cadre du 7e PCRD pour réaliser jusqu'à 20% d’économies d'énergie d’ici 2020. En particulier, selon la directive sur l'efficacité énergétique, les bâtiments sont majoritairement responsables de 40% des dépenses énergétiques en Europe et de 36% des émissions de CO2 ; c’est la raison pour laquelle un ensemble d’initiatives européennes dans le cadre du 7ième PCRD favorise l'utilisation de technologie intelligente dans les bâtiments et rationalise les règles existantes. Energy IN TIME est l'un des projets axés sur l'élaboration d'une méthode de contrôle basée sur la simulation intelligente de l'énergie qui permettra de réduire la consommation des bâtiments non résidentiels. Ce mémoire de thèse propose plusieurs solutions novatrices pour réaliser les objectifs du projet mandaté à l'Université de Lorraine. Les solutions développées dans le cadre de ce projet devraient être validées sur différents sites européens de démonstration. Une première partie présente l'analyse détaillée de ces sites de démonstration et leurs contraintes respectives. Un cadre général correspondant à la construction type de ces sites a été élaboré pour simuler leur comportement. Ce cadre de construction de référence sert de banc d'essai pour la validation des solutions proposées dans ce travail de thèse. Sur la base de la conception de la structure de construction de référence, nous présentons une formulation de contrôle économique utilisant un modèle de contrôle prédictif minimisant la consommation d'énergie. Ce contrôle optimal possède des propriétés de contrôle conscientes de la maintenance. En outre, comme les bâtiments sont des systèmes complexes, les occurrences de pannes peuvent entraîner une détérioration de l'efficacité énergétique ainsi que du confort thermique pour les occupants à l'intérieur des bâtiments. Pour résoudre ce problème, nous avons élaboré une stratégie de diagnostic des dysfonctionnements et une stratégie de contrôle adaptatif des défauts basé sur le modèle économique ; les résultats en simulation ont été obtenus sur le bâtiment de référence. En outre, l'application des solutions proposées peut permettre de relever des défis ambitieux en particulier dans le cas de bâtiments à grande échelle. Dans la partie finale de cette thèse, nous nous concentrons sur le contrôle économique des bâtiments à grande échelle en formulant une approche novatrice du contrôle prédictif de mode réparti. Cette formule de contrôle distribué présente de nombreux avantages tels que l'atténuation de la propagation des défauts, la flexibilité dans la maintenance du bâtiment et les stratégies simplifiées de contrôle du plug-and-play. Enfin, une attention particulière est accordée au problème d'estimation des mesures dont le nombre est limité sur des bâtiments à grande échelle. Les techniques d'estimation avancées proposées sont basées sur les méthodologies de l'horizon mobile. Leur efficacité est démontrée sur les systèmes de construction de référence<br>Since the last two decades, there has been a growing awareness about the climate change and global warming that has instigated several Directorate initiatives from various administrations. These initiatives mainly deal with controlling greenhouse gas emissions, use of non-conventional energy resources and optimization of energy consumption in the existing systems. The European Union has proposed numerous projects under FP7 framework to achieve the energy savings up to 20% by the year 2020. Especially, stated by the Energy Efficiency Directive, buildings are majorly responsible for 40% of energy resources in Europe and 36% of CO2 emission. Hence a class of projects in the FP7 framework promotes the use of smart technology in the buildings and the streamline existing rules. Energy IN TIME is one of the projects focused on developing a Smart Energy Simulation Based Control method which will reduce the energy consumption in the operational stage of existing non-residential buildings. Essentially, this thesis proposes several novel solutions to fulfill the project objectives assigned to the University of Lorraine. The developed solutions under this project should be validated on the demonstration sites from various European locations. We design a general benchmark building framework to emulate the behavior of demonstration sites. This benchmark building framework serves as a test bench for the validation of proposed solutions given in this thesis work. Based on the design of benchmark building layout, we present an economic control formulation using model predictive control minimizing the energy consumption. This optimal control has maintenance-aware control properties. Furthermore, as in buildings, fault occurrences may result in deteriorating the energy efficiency as well as the thermal comfort for the occupants inside the buildings. To address this issue, we design a fault diagnosis and fault adaptive control techniques based on the model predictive control and demonstrate the simulation results on the benchmark building. Moreover, the application of these proposed solutions may face great challenges in case of large-scale buildings. Therefore, in the final part of this thesis, we concentrate on the economic control of large-scale buildings by formulating a novel approach of distributed model predictive control. This distributed control formulation holds numerous advantages such as fault propagation mitigation, flexibility in the building maintenance and simplified plug-and-play control strategies, etc... Finally, a particular attention is paid to the estimation problem under limited measurements in large-scale buildings. The suggested advanced estimation techniques are based on the moving horizon methodologies and are demonstrated on the benchmark building systems
34
Darure, Tejaswinee. "Contribution à l’optimisation de la performance énergétique des bâtiments de grande dimension : une approche intégrée diagnostic / commande économique et coopérative à horizon glissant." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0142.
Full textAbstract:
Au cours des deux dernières décennies, la prise de conscience du changement climatique et des conséquences du réchauffement climatique a incité diverses institutions à prendre de nouvelles directives. Ces directives portent principalement sur le contrôle des émissions des gaz à effet de serre, sur l'utilisation des ressources énergétiques non conventionnelles et l'optimisation de la consommation d'énergie dans les systèmes existants. L'Union européenne a proposé de nombreux projets dans le cadre du 7e PCRD pour réaliser jusqu'à 20% d’économies d'énergie d’ici 2020. En particulier, selon la directive sur l'efficacité énergétique, les bâtiments sont majoritairement responsables de 40% des dépenses énergétiques en Europe et de 36% des émissions de CO2 ; c’est la raison pour laquelle un ensemble d’initiatives européennes dans le cadre du 7ième PCRD favorise l'utilisation de technologie intelligente dans les bâtiments et rationalise les règles existantes. Energy IN TIME est l'un des projets axés sur l'élaboration d'une méthode de contrôle basée sur la simulation intelligente de l'énergie qui permettra de réduire la consommation des bâtiments non résidentiels. Ce mémoire de thèse propose plusieurs solutions novatrices pour réaliser les objectifs du projet mandaté à l'Université de Lorraine. Les solutions développées dans le cadre de ce projet devraient être validées sur différents sites européens de démonstration. Une première partie présente l'analyse détaillée de ces sites de démonstration et leurs contraintes respectives. Un cadre général correspondant à la construction type de ces sites a été élaboré pour simuler leur comportement. Ce cadre de construction de référence sert de banc d'essai pour la validation des solutions proposées dans ce travail de thèse. Sur la base de la conception de la structure de construction de référence, nous présentons une formulation de contrôle économique utilisant un modèle de contrôle prédictif minimisant la consommation d'énergie. Ce contrôle optimal possède des propriétés de contrôle conscientes de la maintenance. En outre, comme les bâtiments sont des systèmes complexes, les occurrences de pannes peuvent entraîner une détérioration de l'efficacité énergétique ainsi que du confort thermique pour les occupants à l'intérieur des bâtiments. Pour résoudre ce problème, nous avons élaboré une stratégie de diagnostic des dysfonctionnements et une stratégie de contrôle adaptatif des défauts basé sur le modèle économique ; les résultats en simulation ont été obtenus sur le bâtiment de référence. En outre, l'application des solutions proposées peut permettre de relever des défis ambitieux en particulier dans le cas de bâtiments à grande échelle. Dans la partie finale de cette thèse, nous nous concentrons sur le contrôle économique des bâtiments à grande échelle en formulant une approche novatrice du contrôle prédictif de mode réparti. Cette formule de contrôle distribué présente de nombreux avantages tels que l'atténuation de la propagation des défauts, la flexibilité dans la maintenance du bâtiment et les stratégies simplifiées de contrôle du plug-and-play. Enfin, une attention particulière est accordée au problème d'estimation des mesures dont le nombre est limité sur des bâtiments à grande échelle. Les techniques d'estimation avancées proposées sont basées sur les méthodologies de l'horizon mobile. Leur efficacité est démontrée sur les systèmes de construction de référence<br>Since the last two decades, there has been a growing awareness about the climate change and global warming that has instigated several Directorate initiatives from various administrations. These initiatives mainly deal with controlling greenhouse gas emissions, use of non-conventional energy resources and optimization of energy consumption in the existing systems. The European Union has proposed numerous projects under FP7 framework to achieve the energy savings up to 20% by the year 2020. Especially, stated by the Energy Efficiency Directive, buildings are majorly responsible for 40% of energy resources in Europe and 36% of CO2 emission. Hence a class of projects in the FP7 framework promotes the use of smart technology in the buildings and the streamline existing rules. Energy IN TIME is one of the projects focused on developing a Smart Energy Simulation Based Control method which will reduce the energy consumption in the operational stage of existing non-residential buildings. Essentially, this thesis proposes several novel solutions to fulfill the project objectives assigned to the University of Lorraine. The developed solutions under this project should be validated on the demonstration sites from various European locations. We design a general benchmark building framework to emulate the behavior of demonstration sites. This benchmark building framework serves as a test bench for the validation of proposed solutions given in this thesis work. Based on the design of benchmark building layout, we present an economic control formulation using model predictive control minimizing the energy consumption. This optimal control has maintenance-aware control properties. Furthermore, as in buildings, fault occurrences may result in deteriorating the energy efficiency as well as the thermal comfort for the occupants inside the buildings. To address this issue, we design a fault diagnosis and fault adaptive control techniques based on the model predictive control and demonstrate the simulation results on the benchmark building. Moreover, the application of these proposed solutions may face great challenges in case of large-scale buildings. Therefore, in the final part of this thesis, we concentrate on the economic control of large-scale buildings by formulating a novel approach of distributed model predictive control. This distributed control formulation holds numerous advantages such as fault propagation mitigation, flexibility in the building maintenance and simplified plug-and-play control strategies, etc... Finally, a particular attention is paid to the estimation problem under limited measurements in large-scale buildings. The suggested advanced estimation techniques are based on the moving horizon methodologies and are demonstrated on the benchmark building systems
35
Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Full textAbstract:
In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
36
Sun, Yong. "Reliability prediction of complex repairable systems : an engineering approach." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16273/.
Full textAbstract:
This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.
37
Sun, Yong. "Reliability prediction of complex repairable systems : an engineering approach." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16273/1/Yong_Sun_Thesis.pdf.
Full textAbstract:
This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.
38
Fridholm, Victoria. "IMPROVE MAINTENANCE EFFECTIVENESS AND EFFICIENCY BY USING HISTORICAL BREAKDOWN DATA FROM A CMMS : Exploring the possibilities for CBM in the Manufacturing Industry." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39816.
Full textAbstract:
Purpose: Explore how historical data from a CMMS can be used in order to improve maintenance effectiveness and efficiency of activities, and investigate the possibilities for CBM in the manufacturing industry in the context of digitalization. Research questions: RQ1: To what extent could condition-based maintenance or other maintenance types being used in order to predict, prevent or in other way eliminate historical breakdowns/faults? RQ2: Which significance has an organization's degree of maturity to reduce the number of breakdowns? Method: A case study was performed at Volvo Construction Equipment Operations in Eskilstuna, who manufactures machinery for the construction industry. The case study was compiled in two phases. Phase one was a quantitative study where raw data were collected from a CMMS and tabulated in order to later perform in-depth analysis. Phase two was designed to collect information that generated a wider understanding of the research area, by performing interviews and observations. A literature study was performed to compare the empirical findings with peer-reviewed information to ensure the quality of the study. The data is compiled and analyzed with an abductive approach. The analysis was followed by a discussion of how the research findings could support identifying possibilities of different maintenance types in the future. Conclusion: The result showed that using historical breakdown data from a CMMS can be useful in order to identify organization’s current state and what possibilities different maintenance types have to decrease the number of breakdowns. To what extent the breakdowns can be decreased relies not only on the maintenance type but also an organizations maturity level. The case study´s result showed that by combining different maintenance types and increasing degree of maturity, Volvo could decrease the historical breakdowns with 86,5%. By only using CBM with current maturity level, 56% of the historical breakdowns could be predicted. However, to decide how many breakdowns that is cost-effective to prevent and precisely what maintenance type that should be used requires a cost analysis which this study is not covering.
39
Diez, Laëtitia. "Apport de la maintenance prévisionnelle au paradigme de régénération industrielle." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0348/document.
Full textAbstract:
Les travaux présentés dans cette thèse portent sur la définition du paradigme de régénération issu des principes du développement durable et de l’économie circulaire. L’idée est de limiter l’épuisement des ressources de la sphère naturelle en exploitant les gisements de «déchets» de la sphère technique et en diminuant la production de déchets. La notion de régénération émerge d’une analogie entre la sphère naturelle et la sphère technique, et fait apparaitre les concepts de «nutriments» et de «décomposeurs». Le concept de nutriment permet de revisiter la fin de vie d’équipements domestiques ou industriels, en les voyant comme des nutriments techniques capables de nourrir certaines filières industrielles après transformation. Ces transformations sont opérées par des décomposeurs dans le milieu naturel, et des régénérateurs dans le milieu industriel. Quatre types de régénérateurs ont été définis. Pour chaque type de régénérateurs, des exigences sur le produit devant être satisfaites par les produits «déchets» sont identifiées afin d’assurer la régénération de ces produits. D’autres exigences ont été définies au niveau du processus de régénération pour spécifier les actions de régénération. Ces deux types d’exigences doivent être maintenus tout au long du cycle de vie du produit. La maintenance a été identifiée comme le processus fondamental pour surveiller et maintenir au travers du temps la capacité de régénération et ainsi prolonger la durée de vie des produits. Lorsque les régénérateurs mettent en place une stratégie de régénération, il est nécessaire d’évaluer sa faisabilité. Nous avons donc proposé un outil d’aide à la décision, en modélisant d’une part le comportement du produit au cours de son cycle de vie, ainsi que le comportement des régénérateurs, et d’autre part l’effet des exigences sur le produit et sur le processus. Les modèles choisis sont les System Dynamics qui permettent la modélisation et la simulation des interactions entre les variables d’un système complexe. Cet outil d’aide à la décision permet d’éprouver les propositions de la thèse, au tour d’un exemple de régénération d’un D3E<br>This thesis is dedicated to the regeneration paradigm from the principles of sustainable development and circular economy. The intention is to limit the exhaustion of natural resources and to reduce waste production through exploiting “waste” deposits of technosphere. The notion of regeneration emerges from an analogy between the natural and technical sphere. It brings to light the concepts of “nutrients” and “decomposers”. The nutrient concept enables to revisit the end of life of domestic or industrial equipment. These equipment are seen as technical nutrients that can feed industrial sectors. The regenerators, called decomposers in nature, are intermediates processors to process “waste” in technical nutrients. Four regenerators have been identified. For each regenerator, product requirements are identified to ensure the regeneration of “waste” product. Additional requirements have been defined in the regeneration process to specify regeneration actions. These two types of requirements must be maintained throughout the product lifecycle. Maintenance process has been identified as fundamental industrial process to monitor and maintain over time the regeneration capability of a product. When regenerators implement a regeneration strategy, it is necessary to assess its feasibility. That is why we proposed a decision- making tool by modeling the product’s behavior during its lifecycle and the regenerator’s behavior, and the effect of the requirements on the product and process. The selected models are System Dynamics. These models allow the modeling and simulation of interactions between the variables of a complex system. The decision-making tool allow test the proposals of this thesis through a regeneration example of an electronic waste
40
Abdennadher, Karim. "ETUDE ET ELABORATION D'UN SYSTEME DE MAINTENANCE PREDICTIVE POUR LES CONDENSATEURS ET LES BATTERIES UTILISES DANS LES ALIMENTATIONS SANS INTERRUPTIONS (ASI)." Phd thesis, Université Claude Bernard - Lyon I, 2010. http://tel.archives-ouvertes.fr/tel-00532642.
Full textAbstract:
Pour assurer une énergie électrique de qualité et de façon permanente, il existe des systèmes électroniques d'alimentation spécifiques. Il s'agit des Alimentations Sans Interruptions (ASI). Une ASI comme tout autre système peut tomber en panne ce qui peut entrainer une perte de redondance. Cette perte induit une maintenance corrective donc une forme d'indisponibilité ce qui représente un coût. Nous proposons dans cette thèse de travailler sur deux composants parmi les plus sensibles dans les ASI à savoir les condensateurs électrolytiques et les batteries au plomb. Dans une première phase, nous présentons, les systèmes de surveillance existants pour ces deux composants en soulignant leurs principaux inconvénients. Ceci nous permet de proposer le cahier des charges à mettre en oeuvre. Pour les condensateurs électrolytiques, nous détaillons les différentes étapes de caractérisation et de vieillissement ainsi que la procédure expérimentale de vieillissement standard accéléré et les résultats associés. D'autre part, nous présentons les résultats de simulation du système de surveillance et de prédiction de pannes retenu. Nous abordons la validation expérimentale en décrivant le système développé. Nous détaillons les cartes électroniques conçues, les algorithmes mis en oeuvre et leurs contraintes d'implémentation respectifs pour une réalisation temps réel. Enfin, pour les batteries au plomb étanches, nous présentons les résultats de simulation du système de surveillance retenu permettant d'obtenir le SOC et le SOH. Nous détaillons la procédure expérimentale de vieillissement en cycles de charge et décharge de la batterie nécessaire pour avoir un modèle électrique simple et précis. Nous expliquons les résultats expérimentaux de vieillissement pour finir avec des propositions d'amélioration de notre système afin d'obtenir un SOH plus précis.
41
Tanzariello, Roberta. "Condition Monitoring of a Belt-Based Transmission System for Comau Racer3 Robots." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14354/.
Full textAbstract:
This project has been developed in collaboration with Comau Robotics S.p.a and the main goal is the development in China of an Health Monitoring Pro-cess using vibration analysis. This project is connected to the activity of Cost Reduction carried out by the PD Cost Engineering Department in China.
The Project is divided in two part:
1. Data Acquisition
2. Data Analysis
An Automatic Acquisition of the moni.log file is carried out and is discussed in Chapter 1. As for the Data Analysis is concerned a data driven approach is considered and developed in frequency domain through the FFT transform and in time domain using the Wavelet transform.
In Chapter 2 a list of the techiques used nowadays for the Signal Analysis and the Vibration Monitoring is shown in time domain, frequency domain and time-frequency domain.
In Chapter 3 the state of art of the Condition Monitoring of all the possible ma-chinery part is carried out from the evaluation of the spectrum of the current and speed.
In Chapter 4 are evaluated disturbances that are not related to a fault but be-long to a normal behaviour of the system acting on the measured forces. Motor Torque Ripple and Output Noise Resolution are disturbance dependent on ve-locity and are mentioned in comparison to the one related to the configuration of the Robot.
In Chapter 5 a particular study case is assigned: the noise problem due to belt-based power transmission system of the axis three of a Racer 3 Robot in Endu-rance test. The chapter presents the test plan done including all the simula-tions.
In Chapter 6 all the results are shown demostrating how the vibration analysis carried out from an external sensor can be confirmed looking at the spectral content of the speed and the current.
In the last Chapter the final conclusions and a possible development of this thesis are presented considering both a a Model of Signal and a Model Based approach.
42
Lima, Fabiana Pereira de. "Simula??o do sistema de esgotamento sanit?rio de Ponta Negra- Natal: mitiga??o dos riscos ambientais e estrat?gia de manuten??o preditiva." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15997.
Full textAbstract:
Made available in DSpace on 2014-12-17T15:03:31Z (GMT). No. of bitstreams: 1
FabianaPLM_DISSERT.pdf: 3435369 bytes, checksum: eeb98ff1cae8e980fe5be54da04a6770 (MD5)
Previous issue date: 2012-08-31<br>The simulation of SES-Natal Ponta Negra: mitigation of environmental risks and predictive maintenance strategy was developed in the context of several operational irregularities in the pumping stations and sewage systems in the system Ponta Negra. Thus, the environmental risks and complaints against the company due to overflows of sewage into the public thoroughfare became common. This neighborhood has shown in recent years an increase of resident higher than the initial expectation of growth. In this sense presumed the large population growth and generation of sewers higher than expected, associated to the use of corrective maintenance and misuse of the system may be the main causes of operational failures occurring in the SES. This study aimed at analyzing the hydraulic behavior of SES Ponta Negrathrough numerical simulation of its operation associated to future scenarios of occupation. The SES Ponta Negra has a long lengthof collection networks and 6 pumping stations interconnected, being EE 1, 2, 4 coastal way, and beach Shopping interconnected EE3 to receives all sewers pumped from the rest pumping station and pumped to the sewage treatment station of neighborhood which consists of a facultative pond followed by three maturation ponds with disposal of treated effluent into infiltration ditches. Oncethey are connected with each other, the study was conducted considering the days and times of higher inflow for all lifts. Furthermore, with the aim of measuring the gatherer network failures were conducted data survey of on the networks. Thephysical and operational survey data was conducted between January/2011 and janeiro/2012. The simulation of the SES was developed with the aim ofdemonstrating its functioning, eithercurrently and in the coming years, based in population estimates and sewage flow. The collected data represents the current framework of the pumping stations of the SES Ponta Negra and served as input to the model developed in MS Excel ? spreadsheet which allowed simulating the behavior of SES in future scenarios. The results of this study show thatBeach Shopping Pumping Station is actually undersized and presents serious functioning problemsthatmay compromise the environmental quality of surrounding area. The other pumping stations of the system will reach itsmaximum capacity between 2013 and 2015, although the EE1 and EE3 demonstrateoperation capacity, even precariously, until 2017. Moreover, it was observed that the misuse of the network system, due to the input of both garbage and stormwater, are major factors of failures that occur in the SES. Finally, it was found that the corrective maintenance appliance, rather than predictive,has proven to beinefficient because of the serious failuresin the system, causing damage to the environment and health risks to users<br>A Simula??o do SES Ponta Negra- Natal: mitiga??o dos riscos ambientais e estrat?gia de manuten??o preditiva foi desenvolvida no contexto de diversas irregularidades operacionais nas esta??es elevat?rias e redes coletoras de esgoto no sistema de Ponta Negra. Com isso, os riscos ambientais e as denuncias contra a empresa de saneamento devido aos extravasamentos de esgotos em via p?blica se tornaram comuns. Esse bairro vem apresentando nos ?ltimos anos um aumento populacional muito maior do que a expectativa inicial de crescimento. Nesse sentido, sup?e-se o grande crescimento populacional e de gera??o de esgotos acima do esperado, atrelado ? utiliza??o de manuten??o corretiva e o mau uso do sistema podem ser as principais causas das falhas operacionais ocorridas neste SES. Esse estudo teve por objetivo analisar o comportamento hidr?ulico do SES de Ponta Negra a partir da simula??o num?rica do seu funcionamento associados ? cen?rios futuros de ocupa??o. O SES Ponta Negra apresenta quil?metros de redes coletoras e 6 esta??es elevat?rias interligadas, sendo a EE 1, 2, 4, Via Costeira e Praia Shopping interligadas a EE3, qual recebe todos os efluentes das demais elevat?rias e bombeia para a esta??o de tratamento de esgotos do bairro a qual ? constitu?da de uma lagoa facultativa seguida de 3 lagoas de matura??o com disposi??o dos efluentes tratados em valas de infiltra??o. Por serem interligadas entre si, o estudo foi realizado considerando os dias e horas de maior vaz?o afluente para todas as elevat?rias. Al?m disso, a fim de mensurar as falhas nas redes coletoras foram realizados levantamento dos dados de obstru??es nessas redes. O levantamento e coleta de dados f?sicos e operacionais foram realizados entre janeiro/2011 e janeiro/2012. A simula??o do SES foi desenvolvida, a fim de nos mostrar como esse est? funcionando atualmente, e como ir? funcionar nos pr?ximos anos, com base na estimativa populacional e de vaz?o de esgotos. Os dados coletados representam o quadro atual das esta??es elevat?rias do SES Ponta Negra e alimentaram o modelo num?rico desenvolvido em planilha eletr?nica MS Excel? que permitiu simular o comportamento do SES em cen?rios futuros. Os resultados obtidos neste estudo mostram que a elevat?ria Praia Shopping j? est? subdimensionada e apresenta falhas graves de funcionamento que compromete a qualidade do ambiente os aspectos ambientais da ?rea onde est? inserida. As demais elevat?rias do sistema atingir?o sua capacidade m?xima segura de bombeamento entre os anos de 2013 e 2015, embora a EE 1 e EE 3 tenham a capacidade de operar, ainda que de forma muito prec?ria, at? o ano de 2017. Al?m disso, observou-se que o mau uso do sistema, com a entrada de lixo e ?gua de chuva na rede coletora, s?o fatores determinantes para as falhas que ocorrem no SES. E finalmente, constatou-se que a utiliza??o de manuten??o corretiva, em vez da preditiva, no sistema como um todo tem se mostrado altamente ineficiente causando graves falhas no sistema, gerando com isso danos ao meio ambiente e riscos sanit?rios aos clientes
43
Diez, Laëtitia. "Apport de la maintenance prévisionnelle au paradigme de régénération industrielle." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0348.
Full textAbstract:
Les travaux présentés dans cette thèse portent sur la définition du paradigme de régénération issu des principes du développement durable et de l’économie circulaire. L’idée est de limiter l’épuisement des ressources de la sphère naturelle en exploitant les gisements de «déchets» de la sphère technique et en diminuant la production de déchets. La notion de régénération émerge d’une analogie entre la sphère naturelle et la sphère technique, et fait apparaitre les concepts de «nutriments» et de «décomposeurs». Le concept de nutriment permet de revisiter la fin de vie d’équipements domestiques ou industriels, en les voyant comme des nutriments techniques capables de nourrir certaines filières industrielles après transformation. Ces transformations sont opérées par des décomposeurs dans le milieu naturel, et des régénérateurs dans le milieu industriel. Quatre types de régénérateurs ont été définis. Pour chaque type de régénérateurs, des exigences sur le produit devant être satisfaites par les produits «déchets» sont identifiées afin d’assurer la régénération de ces produits. D’autres exigences ont été définies au niveau du processus de régénération pour spécifier les actions de régénération. Ces deux types d’exigences doivent être maintenus tout au long du cycle de vie du produit. La maintenance a été identifiée comme le processus fondamental pour surveiller et maintenir au travers du temps la capacité de régénération et ainsi prolonger la durée de vie des produits. Lorsque les régénérateurs mettent en place une stratégie de régénération, il est nécessaire d’évaluer sa faisabilité. Nous avons donc proposé un outil d’aide à la décision, en modélisant d’une part le comportement du produit au cours de son cycle de vie, ainsi que le comportement des régénérateurs, et d’autre part l’effet des exigences sur le produit et sur le processus. Les modèles choisis sont les System Dynamics qui permettent la modélisation et la simulation des interactions entre les variables d’un système complexe. Cet outil d’aide à la décision permet d’éprouver les propositions de la thèse, au tour d’un exemple de régénération d’un D3E<br>This thesis is dedicated to the regeneration paradigm from the principles of sustainable development and circular economy. The intention is to limit the exhaustion of natural resources and to reduce waste production through exploiting “waste” deposits of technosphere. The notion of regeneration emerges from an analogy between the natural and technical sphere. It brings to light the concepts of “nutrients” and “decomposers”. The nutrient concept enables to revisit the end of life of domestic or industrial equipment. These equipment are seen as technical nutrients that can feed industrial sectors. The regenerators, called decomposers in nature, are intermediates processors to process “waste” in technical nutrients. Four regenerators have been identified. For each regenerator, product requirements are identified to ensure the regeneration of “waste” product. Additional requirements have been defined in the regeneration process to specify regeneration actions. These two types of requirements must be maintained throughout the product lifecycle. Maintenance process has been identified as fundamental industrial process to monitor and maintain over time the regeneration capability of a product. When regenerators implement a regeneration strategy, it is necessary to assess its feasibility. That is why we proposed a decision- making tool by modeling the product’s behavior during its lifecycle and the regenerator’s behavior, and the effect of the requirements on the product and process. The selected models are System Dynamics. These models allow the modeling and simulation of interactions between the variables of a complex system. The decision-making tool allow test the proposals of this thesis through a regeneration example of an electronic waste
44
Sajeva, Lisa. "Predizione del tempo rimanente di vita di un impianto mediante Hidden Markow Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13846/.
Full textAbstract:
In this thesis we investigate the main methods used in the literature for the automation of conditio-base maintenance and then see a pratical application concerning bearing system. In the specifics we first analyze the row signal of vibration decomposing whit a wavelet packet transform then, we select the best level and index in term of characteristics. For create a model of failure we use the method of Hidden Markov Model. At least we compare the model generated with other level and index of decomposition to demonstrate that our choice was the best.
45
Azeli, Nourelhouda. "Maintenance prévisionnelle des systèmes de production géographiquement distribués sous ressources limitées." Electronic Thesis or Diss., Troyes, 2022. http://www.theses.fr/2022TROY0017.
Full textAbstract:
Cette thèse aborde la problématique de l’aide à la décision de maintenance prévisionnelle pour des systèmes de production géographiquement dispersés (GDPS). La structure des GDPS représente un défi important pour l'établissement de stratégies de maintenance efficaces et des stratégies de maintenance prévisionnelle sont particulièrement adaptées. Cependant, la question de la disponibilité des ressources de maintenance doit être analysée et intégrée. Dans cette thèse, nous proposons trois politiques de maintenance prévisionnelles prenant en compte des ressources limitées de maintenance pour un GDPS dont les sites de production se dégradent graduellement. Les trois politiques proposées visent à optimiser un critère économique en sélectionnant l'ensemble de sites à maintenir. Les deux premières politiques s’appuient sur des données d’inspections périodiques. La première politique sélectionne pour la maintenance, la permutation des sites qui maximise la fiabilité du système après réparation, sans prise en compte des distances. La deuxième politique construit la tournée des sites à maintenir en tenant compte des ressources disponibles et des distances entre les sites. Enfin, la troisième politique est une politique dynamique. Elle se base sur des données de surveillance en temps réel des niveaux de dégradation pour adapter la tournée. Nous avons utilisé la simulation Monte Carlo pour évaluer le critère économique asymptotique. L’efficacité des politiques proposées est démontrée par comparaison avec des politiques plus classiques<br>This thesis addresses the problem of predictive maintenance decision making for geographically dispersed production systems (GDPS). The structure of GDPS represents an important challenge for the establishment of efficient maintenance strategies. Predictive maintenance strategies are particularly suitable. However, the issue of the availability of maintenance resources must be analyzed and integrated. In this thesis, we propose three predictive maintenance policies considering limited maintenance resources for a GDPS with gradually degrading production sites. The three proposed policies aim at optimizing an economic criterion by selecting the set of sites to be maintained. The first two policies are based on periodic inspection data. The first policy selects for maintenance, the permutation of sites that maximizes the reliability of the system after repair, without considering the distances. The second policy constructs the tour of sites to be maintained from the available resources and the distances between sites. Finally, the third policy is a dynamic policy. It relies on real-time monitoring data of degradation levels to adapt the tour. We used Monte Carlo simulation to evaluate the asymptotic economic criterion. The effectiveness of the proposed policies is demonstrated by comparison with more conventional policies
46
Callow, Daniel John. "Optimisation of the Neural Network Process for an Improved Bridge Deterioration Model." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/367038.
Full textAbstract:
Infrastructure maintenance is a vital aspect for any country to ensure safety and reliability of its infrastructure and the population which use these assets. To ensure that the highest degree of maintenance is performed and recorded for infrastructure, Bridge Management Systems (BMSs) have been developed to allow bridge agencies to have an effective means to determine and understand the best decisions to make for infrastructure maintenance.
Various models have been developed for the BMS with the most typical approach being the stochastic Markovian-based method, using currently retrieved bridge data as inputs for predicting the bridges’ future deterioration levels. However, a drawback to this method is the disregard for historical data as references to future predictions. This situation has led to the advancement of BMSs to incorporate Artificial Neural Network (ANN) processes as a means of predicting future bridge deterioration levels.
This advancement in ANN-based BMSs is an improvement over the typical model due to the incorporation of historical data curves. However, a drawback to this is the fact that biannual bridge inspection data has only started to be collected within the past 10-20 years, limiting the inputs for ANN methods. Further research into ANN models has developed a means of deriving the missing historical data through the use of current bridge inspection data and non-bridge data collected from various sources. This method is referred to as the Backwards-Prediction Model (BPM) and is an effective method for determining this missing historical data for subsequent use as inputs to further ANN methods for future prediction.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Science, Environment, Engineering and Technology<br>Full Text
47
Krause, Jakob. "Kontextsensitive Prognoseverfahren für das Abnutzungsverhalten von technischen Systemen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130908.
Full textAbstract:
Technische Systeme nutzen sich ab. Dadurch bedingt kommt es zu Ausfällen. Um die Funktionstüchtigkeit von abgenutzten technischen Systemen wiederherzustellen, werden Instandsetzungsmaßnahmen durchgeführt. Da die Folgen eines unerwartet eintretenden Ausfalls drastisch sein können, ist es sinnvoll, das Abnutzungsverhalten eines technischen Systems vorherzusagen und so den Zeitpunkt von Instandsetzungsmaßnahmen zielgerichtet zu planen. Die Erstellung von Abnutzungsprognosen wird dadurch erschwert, dass sich technische Systeme oft variabel, in Abhängigkeit von auf sie einwirkenden Beanspruchungen, abnutzen. Außerdem wird diese Abnutzungsvariabilität von betriebsbedingten Einflüssen überlagert, was deren Modellierung erschwert. Im Rahmen dieser Arbeit wurden deshalb Lösungsansätze entwickelt, die es ermöglichen, die Abnutzungsvariabilität eines technischen Systems in Abnutzungsprognosen zu integrieren und dabei betriebsbedingte Einflüsse zu berücksichtigen. Somit können Instandsetzungsmaßnahmen präziser geplant, Ressourcen geschont sowie Kosten reduziert werden<br>Technical systems are prone to deterioration. This leads to negative consequences like break-downs. Maintenance actions are executed in order to transfer technical systems back into healthy states. If break downs occur suddenly, the consequences can be dramatic. Therefore, it is reasonable to schedule maintenance actions based on health-state predictions. Thereby, health state predictions are impeded by the fact that technical systems often deteriorate variably, depending on certain stress factors. Furthermore, the effects of variable deterioration behavior can be hidden by system specific behavior. Thus, approaches are shown which integrate variable deterioration behavior into healthstate predictions while influences caused by the system specific behavior are considered. Consequently, maintenance actions can be scheduled more efficiently which spares resources and reduces costs
48
Liu, Yinling. "Conception et vérification du système d'Information pour la maintenance aéronautique." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI133.
Full textAbstract:
Le soutien opérationnel est l’un des aspects les plus importants pour la maintenance aéronautique. Il vise essentiellement à fournir un portefeuille de services permettant d’implémenter la maintenance avec un niveau élevé d’efficacité, de fiabilité et d’accessibilité. L’une des principales difficultés du support opérationnel est qu’il n’existe pas de plate-forme intégrant tous les processus de maintenance des avions afin de réduire les coûts et d’améliorer le niveau de service. Il est donc nécessaire de réaliser un système autonome de maintenance des avions dans lequel toutes les informations de maintenance peuvent être collectées, organisées, analysées et gérées de manière à faciliter la prise de décision. Pour ce faire, une méthodologie innovante a été proposée, qui concerne la modélisation, simulation, vérification formelle et analyse des performances du système autonome mentionné. Trois axes ont été abordés dans cette thèse. Premier axe concerne la conception et simulation d'un système autonome pour la maintenance aéronautique. Nous proposons une conception innovante d'un système autonome prenant en charge la prise de décision automatique pour la planification de la maintenance. Deuxième axe vise la vérification de modèles sur des systèmes de simulation. Nous proposons une approche plus complète de la vérification des comportements globaux et des comportements opérationnels des systèmes. Troisième axe porte sur l'analyse de la performance des systèmes de simulation. Nous proposons une approche consistant à combiner un système de simulation à base d’agent avec une approche « Fuzzy Rough Nearest Neighbor », afin de mettre en œuvre la classification et prévision efficaces des pannes pour la maintenance des avions avec des données manquantes. Finalement, des modèles et systèmes de la simulation ont été proposés. Des expérimentations de la simulation illustrent la faisabilité de l’approche proposée<br>Operational support is one of the most important aspects of aeronautical maintenance. It aims to provide a portfolio of services to implement maintenance with a high level of efficiency, reliability and accessibility. One of the major difficulties in operational support is that there is no platform that integrates all aircraft maintenance processes in order to reduce costs and improve the level of service. It is therefore necessary to build an autonomous aircraft maintenance system in which all maintenance information can be collected, organized, analyzed and managed in a way that facilitates decision-making. To do this, an innovative methodology has been proposed, which concerns modelling, simulation, formal verification and performance analysis of the autonomous system mentioned. Three axes were addressed in this thesis. The first axis concerns the design and simulation of an autonomous system for aeronautical maintenance. We offer an innovative design of an autonomous system that supports automatic decision making for maintenance planning. The second axis is the verification of models on simulation systems. We propose a more comprehensive approach to verifying global behaviours and operational behaviours of systems. The third axis focuses on the analysis of the performance of simulation systems. We propose an approach of combining an agent-based simulation system with the “Fuzzy Rough Nearest Neighbor” approach, in order to implement efficient classification and prediction of aircraft maintenance failures with missing data. Finally, simulation models and systems have been proposed. Simulation experiments illustrate the feasibility of the proposed approach
49
Hassan, Muhammad. "Production 4.0 of Ring Mill 4 Ovako AB." Thesis, Högskolan i Gävle, Elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33405.
Full textAbstract:
Cyber-Physical System (CPS) or Digital-Twin approach are becoming popular in industry 4.0 revolution. CPS not only allow to view the online status of equipment, but also allow to predict the health of tool. Based on the real time sensor data, it aims to detect anomalies in the industrial operation and prefigure future failure, which lead it towards smart maintenance. CPS can contribute to sustainable environment as well as sustainable production, due to its real-time analysis on production. In this thesis, we analyzed the behavior of a tool of Ringvalsverk 4, at Ovako with its twin model (known as Digital-Twin) over a series of data. Initially, the data contained unwanted signals which is then cleaned in the data processing phase, and only before production signal is used to identify the tool’s model. Matlab’s system identification toolbox is used for identifying the system model, the identified model is also validated and analyzed in term of stability, which is then used in CPS. The Digital-Twin model is then used and its output being analyzed together with tool’s output to detect when its start deviate from normal behavior.
50
Semotam, Petr. "Prediktivní systém údržby obráběcích strojů s využitím vibrodiagnostiky." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382193.
Full textAbstract:
This diploma thesis concerns issues of predictive and condition based maintenance system of machine tools with using a vibrodiagnostics. It studies and researches its impacts through the basic processes of the maintenance system and characterizes the vibration diagnosis as its tool and mean. There is also described a process of putting condition based maintenance into practice in the practical part of the thesis. The development is realized at Siemens Ltd. Brno with all its requirements and aspects such as a maintenance audit which means the decision on the suitability of condition based maintenance within the current maintenance system, technical analysis as a part of introduction of vibration diagnosis and the practical example of acquiring, recording and assessment of measured vibration. Prior to the end the economic evaluation of the planned predictive maintenance system and the design of the general model of development and implementation of the maintenance system into practice are included.