Relevant bibliographies by topics / Predictive maintenance systems

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Predictive maintenance systems'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Predictive maintenance systems"

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B, Selvalakshmi, Vijayalakshmi P, Subha N, and Balamani T. "PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS USING DATA MINING WITH FUZZY LOGIC SYSTEMS." ICTACT Journal on Soft Computing 14, no. 4 (2024): 3361–67. http://dx.doi.org/10.21917/ijsc.2024.0472.

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In industrial systems, predictive maintenance has emerged as a crucial strategy to minimize downtime and optimize operational efficiency. This study explores the utilization of data mining techniques, specifically fuzzy logic systems, for predictive maintenance. The background section examines the importance of predictive maintenance in industrial contexts and highlights the limitations of traditional approaches. The methodology section outlines the process of employing fuzzy logic systems for predictive maintenance, including data preprocessing, feature selection, fuzzy rule generation, and model evaluation. The contribution of this research lies in providing a comprehensive framework for implementing predictive maintenance using fuzzy logic systems, offering insights into the integration of data mining techniques with industrial systems. Results demonstrate the effectiveness of the proposed methodology in accurately predicting maintenance needs and minimizing unplanned downtime. Findings suggest that fuzzy logic systems can enhance predictive maintenance capabilities by handling uncertainties and vagueness inherent in industrial data.
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Möhring, Michael, Rainer Schmidt, Barbara Keller, Kurt Sandkuhl, and Alfred Zimmermann. "Predictive Maintenance Information Systems." International Journal of Enterprise Information Systems 16, no. 2 (2020): 22–37. http://dx.doi.org/10.4018/ijeis.2020040102.

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Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.
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Zhao, Yu, Kakoli Bandyopadhyay, and Cynthia Barnes. "Predictive Maintenance Information Systems." International Journal of Enterprise Information Systems 16, no. 2 (2020): 54–72. http://dx.doi.org/10.4018/ijeis.2020040104.

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Enterprise resource planning (ERP) systems allow businesses to achieve high performance through distinctive capabilities and are one of the fastest growing areas within information systems. Many universities have adopted ERP in their management information systems (MIS) curriculum to increase the marketability of their students. Drawing on the IS success model and several constructive learning theories, this study develops a model that is predictive of students' continued ERP software use intention, satisfaction, and perceived learning outcomes. SAP is the ERP system used in this study. Business students at four mid-sized state universities in the United States were surveyed. The universities are members of the SAP University Alliance. There were 373 usable responses. Partial least squares structural equation modeling (PLS-SEM) was used to empirically test the model. The findings indicate that student motivation, perceived instructor support, and ERP system quality are strong predictors of student satisfaction, and learning outcomes. Student motivation and ERP system quality, but not perceived instructor support, are also significant predictors of continued use intention.
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Kharchenko, K. V., A. Zh Zubets, E. I. Moskvitina, L. K. Babayan, and A. M. Laffah. "Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies." Mining Industry Journal (Gornay Promishlennost), no. 4/2024 (August 23, 2024): 130–38. http://dx.doi.org/10.30686/1609-9192-2024-4-130-138.

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The mining industry plays a key role in the global economy, providing raw materials to various industries. However, the operational efficiency of mining equipment remains a serious issue due to high maintenance costs and downtime caused by its failures. The relevance of the study is defined by the potential of using the Industry 4.0 technologies to improve the efficiency of mining equipment maintenance. The purpose of the work is to evaluate the efficiency of implementing predictive maintenance systems based on the Industry 4.0 technologies and to develop recommendations for their development in the industry. The methodology includes an analysis of the technology adoption level in 2013–2023, collection of the KPI data to assess the impact of predictive maintenance, studying the economic efficiency of investments, the development of models for predicting failures and optimizing maintenance strategies. The results showed a significant increase in the implementation level of the Industry 4.0 technologies, improved KPIs and high economic efficiency of investments in predictive maintenance systems. The developed models demonstrated high accuracy of failure prediction and optimization of the maintenance strategies. Recommendations are formulated for the efficient implementation of predictive maintenance systems with account for the specific features of the industry. The research has theoretical significance for the development of the predictive maintenance concept and practical value for the mining enterprises. Further research may be directed towards the development of the industry standards and the integration of predictive maintenance systems with other management processes.
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Watanabe, Takeshi, and Masahiko Ooishi. "Predictive Maintenance Systems for Substation." IEEJ Transactions on Power and Energy 112, no. 6 (1992): 455–60. http://dx.doi.org/10.1541/ieejpes1990.112.6_455.

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Abdulrazzq, Raghdah Adnan, Nisreen Mustafa Sajid, and Marwan Sabah Hasan. "Artificial intelligence-driven predictive maintenance in IoT systems." South Florida Journal of Development 5, no. 12 (2024): e4781. https://doi.org/10.46932/sfjdv5n12-030.

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The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above results bring out the potential of AI-based solutions for real-time predictive maintenance over complex IoT networks. It concludes by indicating some further research vectors, such as the refinement of the model and the extension of AI-driven predictive maintenance for broader applications in IoT, such as smart cities and healthcare systems.
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Tichý, Tomáš, Jiří Brož, Zuzana Bělinová, and Rastislav Pirník. "Analysis of Predictive Maintenance for Tunnel Systems." Sustainability 13, no. 7 (2021): 3977. http://dx.doi.org/10.3390/su13073977.

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Smart and automated maintenance could make the system and its parts more sustainable by extending their lifecycle, failure detection, smart control of the equipment, and precise detection and reaction to unexpected circumstances. This article focuses on the analysis of data, particularly on logs captured in several Czech tunnel systems. The objective of the analysis is to find useful information in the logs for predicting upcoming situations, and furthermore, to check the possibilities of predictive diagnostics and to design the process of predictive maintenance. The main goal of the article is to summarize the possibilities of optimizing system maintenance that are based on data analysis as well as expert analysis based on the experience with the equipment in the tunnel. The results, findings, and conclusions could primarily be used in the tunnels; secondarily, these principles could be applied in telematics and lead to the optimization and improvement of system sustainability.
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Wöstmann, R., P. Strauss, and J. Prof Deuse. "Predictive Maintenance in der Produktion*/Predictive Maintenance in production." wt Werkstattstechnik online 107, no. 07-08 (2017): 524–29. http://dx.doi.org/10.37544/1436-4980-2017-07-08-48.

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Über neue Möglichkeiten der Vernetzung und Datenverarbeitung von Anlagen und -komponenten gewinnt die prädiktive Instandhaltung stetig an Bedeutung. Während sie insbesondere in der Luftfahrt sowie bei der Versorgungs- und Energietechnik schon seit Jahren zum Einsatz kommt, sind innerhalb der Produktion derzeit noch wenige Anwendungsfälle zu finden. Der Beitrag stellt die bisher ungenutzten Potentiale dar, indem branchenübergreifende Anwendungsfälle sowie deren Übertragbarkeit auf die Produktion und Voraussetzungen für eine erfolgreiche Einführung vorgestellt werden.   Due to new possibilities of connectivity and data processing of assets and components, predictive maintenance has gained a growing importance. While it has been used in aerospace, supply or energy technology for many years, there are still few applications to be found within production. This paper outlines unused potential in presenting a classification of existing predictive maintenance applications and their transferability to production as well as prerequisites for a successful implementation.
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Ryshkovskyi, Oleksandr, and Markiian Lukashiv. "INSTRUMENTAL PLATFORMS FOR VIBRATION ANALYSIS IN PREDICTIVE MAINTENANCE." Measuring Equipment and Metrology 85, no. 2 (2024): 21–28. http://dx.doi.org/10.23939/istcmtm2024.02.021.

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The article explores the benefits and importance of predictive maintenance in Industry 4.0. It is a revolutionary ap- proach that analyzes data from cyber-physical systems to predict possible equipment failures before they occur and technology applied to detect early signs of a vibration problem on equipment. Thus, downtime is minimized and production continuity is ensured.
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Yang, Jia, Yongkui Sun, Yuan Cao, and Xiaoxi Hu. "Predictive Maintenance for Switch Machine Based on Digital Twins." Information 12, no. 11 (2021): 485. http://dx.doi.org/10.3390/info12110485.

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As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.
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Dissertations / Theses on the topic "Predictive maintenance systems"

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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.

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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.
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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.

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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
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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.

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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
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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.

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Bansal, Dheeraj. "An advanced real-time predictive maintenance framework for large scale machine systems." Thesis, Aston University, 2005. http://publications.aston.ac.uk/12235/.

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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.
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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.

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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.
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Yang, Lei. "Methodology of Prognostics Evaluation for Multiprocess Manufacturing Systems." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298043095.

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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.

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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.

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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.

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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.
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Books on the topic "Predictive maintenance systems"

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Lughofer, Edwin, and Moamar Sayed-Mouchaweh, eds. Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2.

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Gouriveau, Rafael, Kamal Medjaher, and Noureddine Zerhouni. From Prognostics and Health Systems Management to Predictive Maintenance 1. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119371052.

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Chebel-Morello, Brigitte, Jean-Marc Nicod, and Christophe Varnier. From Prognostics and Health Systems Management to Predictive Maintenance 2. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119436805.

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Ebeling, Charles E. The determination of operational and support requirements and costs during the conceptual design of space systems: Final report : under grant no. NAG-1-1327. University of Dayton, Engineering Management and Systems Dept., 1992.

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United States. National Aeronautics and Space Administration., ed. The determination of operational and support requirements and costs during the conceptual design of space systems: Interim report. University of Dayton, Engineering Management and Systems Dept., 1991.

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Inglese, Vitaliano, Mario Girone, Marco Pezzetti, and Pasquale Arpaia. Cryogenic Systems: Advanced Monitoring, Fault Diagnostics, and Predictive Maintenance. Momentum Press, 2017.

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Holm-Nielsen, Jens Bo, Sanjeevikumar Padmanaban, K. Mohana Sundaram, and P. Pandiyan. Photovoltaic Systems: Artificial Intelligence-Based Fault Diagnosis and Predictive Maintenance. Taylor & Francis Group, 2022.

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Holm-Nielsen, Jens Bo, Sanjeevikumar Padmanaban, K. Mohana Sundaram, and P. Pandiyan. Photovoltaic Systems: Artificial Intelligence-Based Fault Diagnosis and Predictive Maintenance. Taylor & Francis Group, 2022.

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Holm-Nielsen, Jens Bo, Sanjeevikumar Padmanaban, K. Mohana Sundaram, and P. Pandiyan. Photovoltaic Systems: Artificial Intelligence-Based Fault Diagnosis and Predictive Maintenance. CRC Press LLC, 2022.

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Holm-Nielsen, Jens Bo, Sanjeevikumar Padmanaban, K. Mohana Sundaram, and P. Pandiyan. Photovoltaic Systems: Artificial Intelligence-Based Fault Diagnosis and Predictive Maintenance. Taylor & Francis Group, 2022.

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Book chapters on the topic "Predictive maintenance systems"

1

Sharanya, S., Revathi Venkataraman, and G. Murali. "Predictive Maintenance." In Introduction to AI Techniques for Renewable Energy Systems. CRC Press, 2021. http://dx.doi.org/10.1201/9781003104445-10.

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Liu, Min, Ling Li, and Feng Yan. "Operation Process Control Based on Cyber-Physical Systems." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_11.

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Marzec, Mateusz, Paweł Morkisz, Jakub Wojdyła, and Tadeusz Uhl. "Intelligent Predictive Maintenance System." In Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56994-9_55.

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Lughofer, Edwin, and Moamar Sayed-Mouchaweh. "Prologue: Predictive Maintenance in Dynamic Systems." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_1.

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Ashfahani, Andri, Mahardhika Pratama, Edwin Lughofer, Qing Cai, and Huang Sheng. "An Online RFID Localization in the Manufacturing Shopfloor." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_10.

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Tinga, Tiedo, and Richard Loendersloot. "Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_11.

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Orchard, Marcos E., and David E. Acuña. "On Prognostic Algorithm Design and Fundamental Precision Limits in Long-Term Prediction." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_12.

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Lecuona, Iñigo, Rosa Basagoiti, Gorka Urchegui, Luka Eciolaza, Urko Zurutuza, and Peter Craamer. "Performance Degradation Monitoring and Quantification: A Wastewater Treatment Plant Case Study." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_13.

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Maciel, Leandro, and Rosangela Ballini. "Fuzzy Rule-Based Modeling for Interval-Valued Data: An Application to High and Low Stock Prices Forecasting." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_14.

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Wotawa, Franz. "Reasoning from First Principles for Self-adaptive and Autonomous Systems." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_15.

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Conference papers on the topic "Predictive maintenance systems"

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Pavitha, N., R. M. Savithramma, B. P. Ashwini, Amruta Mankawade, Yojana Naik, and Yashashree Bedmutha. "Intelligent Predictive Maintenance for Smart Building Systems." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760900.

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Selvan, G., V. Anantha Krishna, Thiyagesan M, et al. "IoT-Enabled Predictive Maintenance for Renewable Energy Systems." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894630.

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Thingom, Chintureena, Nmg Kumar, Sreenivasulu Gogula, Aparajita Mukherjee, Supriya Bhosale, and Atul Sarojwal. "Deep Learning for Predictive Maintenance in Power Systems." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877579.

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Barpute, Jyotsna Vilas, Shubhangi Suryawanshi, Vanita Kshirsagar, Digvijay Bhosale, Pramod Patil, and Atharv Patil. "Predictive Maintenance of a Metro's Air Compressor." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689744.

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Kshirsagar, Vanita, Digvijay G. Bhosale, Shantanu Gilbile, Anchal Kharade, Harshal Sasane, and Pratik Dhembare. "Predictive Maintenance in Industrial Machinery using Machine Learning." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007348.

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Chaudhary, Aman, Rupali Rastogi, Abhinav Chola, Pushpinder kaur Josan, and Debasis Biswas. "Cloud based Predictive Maintenance Technique for Aviation System." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717276.

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Manchadi, Oumaima, Zineb El Otmani Dehbi, Fatima-Ezzahraa Ben-Bouazza, Ayman Edder, Idriss Tafala, and Bassma Jioudi. "IoT-Powered Predictive Maintenance Framework for ICU Ventilators." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620162.

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Sharma, Shiv Shankar, Vivek V, and Ashwini Malviya. "AI-Enhanced Predictive Maintenance in Intelligent Systems for Industries." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743977.

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A, Raghuvira Pratap, Satwik Panda, and Syama Sameera G. "Predictive Maintenance for Two-Wheeler Vehicles Using XGBoost." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717187.

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Hutchinson, D. G., and K. A. Lichti. "Integration of Maintenance Programs and Expert Systems." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96364.

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Abstract Computer based maintenance systems have been in use in the dairy industry in New Zealand for several years. The systems provide planning and control facilities suitable for keeping track of engineering maintenance activities. Predictive maintenance techniques which can be linked to maintenance management systems are also becoming available. These include trend analysis systems which collect data and provide trend graphs as well as expert systems which provide interpreted advice for selection and use of materials. Collection of this data and advice in an integrated predictive maintenance package provides opportunities to extend plant life through improvements in quality assurance and quality control in all aspects of materials.
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Reports on the topic "Predictive maintenance systems"

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Wilke, Rudeger. Representing Complex Systems as Graphs for Debugging and Predictive Maintenance-Preliminary Thoughts. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2540215.

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Vuyyuru, Tejaswini. Using Predictive Maintenance techniques and Business Intelligence to develop smarter factory systems for the digital age. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1566.

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Becerra-Stasiewicz,, Natalie, Veronica Gerios, Christina Mayer, and Sean Morefield. Remote monitoring of cathodic protection systems on navigable waterways. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49434.

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Cathodic protection is one of the main modes of corrosion prevention for structures in navigable waterways. The rectifier output voltage must be in a specific range to provide effective protection against corrosion. This effort was designed to monitor, predict, and stabilize the efficacy of multiple cathodic protection systems. Copper/copper-sulfate half-cell electrode sensors, water quality sensors, and gauges for rectifier output were connected to modems at multiple locks so the data could be analyzed to create a predictive maintenance algorithm.
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Latorre, Lucía, Eduardo Rego, Lorenzo De Leo, and Mariana Gutierrez. Tech Report: Digital Twins. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013166.

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Digital twins are finding innovative applications in a wide range of industries. In manufacturing, they enable product design optimization, predictive machinery maintenance, and customized production. Healthcare will benefit from precise diagnostics, personalized treatments, and advanced surgical planning. In city planning, they support efficient urban design and complex situation management. Regarding energy, they promote the efficiency and sustainability of systems and infrastructure. Finally, in the agricultural sector, they improve crop management, resource use and animal welfare.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.

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Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy networks. This research explores the synergistic integration of AI and nanomaterials to develop self-regulating, adaptive, and fault-tolerant energy infrastructures. The study examines AI-powered energy storage, decentralized smart microgrids, quantum AI for grid cybersecurity, and blockchain-integrated energy trading. Furthermore, the report assesses global industry adoption, policy frameworks, and economic growth trends, providing a strategic roadmap for the large-scale implementation of AI-enhanced nanomaterial-based energy systems. Through case studies and real-world applications, this research highlights how AI and nanotechnology will drive the next-generation sustainable energy revolution. Keywords Smart nanomaterials, AI-integrated grids, sustainable renewable energy, graphene-based solar cells, perovskite photovoltaics, quantum dots in energy, MXenes for energy storage, AI-driven energy optimization, machine learning for smart grids, deep learning energy forecasting, predictive maintenance in energy grids, digital twins for grid management, AI-powered decentralized microgrids, blockchain energy trading, hydrogen storage nanomaterials, AI-enhanced lithium-ion batteries, reinforcement learning in energy distribution, AI for demand-side energy management, quantum AI for grid cybersecurity, scalable nanomaterial-based energy solutions, AI-driven self-healing energy materials.
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Kacprzynski, Gregory J., Michael Gumina, Michael J. Roemer, Daniel E. Caguiat, and Thomas R. Galie. A Prognostic Modeling Approach for Predicting Recurring Maintenance for Shipboard Propulsion Systems. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada408968.

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Unknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0011744.

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The objective of the project was to validate existing pipeline integrity prediction models through field testing multiple pipelines, validate the performance of in-line instrumentation through smart pig runs, and finally, to assess the actual integrity of aging damaged and defective pipelines. The objectives were accomplished by the testing of aging out-of-service lines using "smart pigs", followed by hydrotesting of the lines to failure, recovery of the failed sections, and determination of the pipeline characteristics in the vicinity of the failed sections (failure analysis). One objective of the project was to validate the dented, gouged, and corroded pipeline burst strength prediction models currently in existence, such as ASME B31-G, R-Streng, and DNV 99 for pipelines. Another model was being developed as a joint international project sponsored by the U. S. Minerals Management Service, Petroleos Mexicanos (PEMEX), and Instituto Mexicano del Petroleo (IMP) titled RAM PIPE REQUAL and an associated JIP identified as PIMPIS (Pipeline Inspection, Maintenance, and Performance Information System), this would be tested and validated as well. The validation was provided by hydrotesting in-situ pipelines to failure. Sustained and rapidly applied hydro-pressures were used to investigate the effects of delayed and dynamic pressure related failures. After testing, the pipelines were scheduled for decommissioning; with the failed sections located, and brought to the laboratory for testing and analysis. Class A predictions were made before the pipelines were hydrotested to failure based on results from in-line instrumentation (instrumented) and from knowledge of the pipeline products and other characteristics (not instrumented). Based on the results from the testing, the analytical models were to be revised to provide improved agreement between the measured and predicted burst pressures. Since the pipelines were inspected with smart pigs before the hydro-tests, it was possible to compare the smart-pig data gathered during pig runs to the actual condition of the pipeline. This was accomplished by recovering sections of the pipeline that were identified by the pig as having pits or metal-loss areas. Reviewed pipeline decommissioning inventory and selected a pipeline candidate. The specific scope of work included: � Selected pipelines for testing. � Conducted field tests with an instrumented pig to determine pipeline denting, gouging and corrosion conditions. � Used existing analytical models to determine burst strength for both instrumented and non-instrumented pipelines. � Hydrotested the selected pipelines to failure (sustained and rapidly applied pressures). � Located and retrieve failed sections and other sections identified as problem spots by the "smart-pig." � Compared "smart pig" data to actual pipeline condition. � Analyzed the failed sections to determine their physical and material characteristics. � Revised the analytical models to provide improved agreements between predicted and measured burst pressures.
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Ginis, Isaac, Deborah Crowley, Peter Stempel, and Amanda Babson. The impact of sea level rise during nor?easters in New England: Acadia National Park, Boston Harbor Islands, Boston National Historical Park, and Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2304306.

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This study examines the potential impact of sea level rise (SLR) caused by climate change on the effects of extratropical cyclones, also known as nor?easters, in four New England coastal parks: Acadia National Park (ACAD), Boston Harbor Islands National Recreation Area (BOHA), Boston National Historical Park (BOST) and Cape Cod National Seashore (CACO). A multi-method approach is employed, including a literature review, observational data analysis, coupled hydrodynamic-wave numerical modeling, 3D visualizations, and communication of findings. The literature review examines previous studies of nor?easters and associated storm surges in New England and SLR projections across the study domain due to climate change. The observational data analysis evaluates the characteristics of nor?easters and their effects, providing a basis for validating the model. Numerical modeling is performed using the Advanced Circulation (ADCIRC) model, coupled with the Simulating Waves in the Nearshore (SWAN) model to simulate storm surges and waves. The model was validated against available observations and demonstrated its ability to simulate water levels, inland inundation, and wave heights in the study area with high accuracy. The validated model was used to simulate three powerful nor?easters (April 2007, January 2018, and March 2018) and each storm was simulated for three sea levels, (1) a baseline mean sea level representative of the year 2020, as well as with a (2) 1 ft of SLR and (3) 1 m of SLR. Analysis of the model output was used to assess the vulnerability of the parks to nor?easters by examining peak impacts in the park areas. Additional simulations were conducted to evaluate the role of waves in predicting peak water levels and the impact of inlet configurations on storm surges within coastal embayments behind the barrier beach systems in the southern Cape Cod region. The project developed maps, three-dimensional visualizations, and an interpretive film to assist the parks in planning for resource management, maintenance, emergency management, visitor access, safety, education, and outreach. These tools provide a better understanding of the potential impacts of nor?easters and SLR and enable the parks to better prepare for future storms.
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