Relevant bibliographies by topics / Predictive maintenance

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  2. Dissertations / Theses
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Academic literature on the topic 'Predictive maintenance'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 January 2025

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Journal articles on the topic "Predictive maintenance"

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Ratul, MD Rakibul Islam. "MMS Predictive Maintenance Big Data Analytics." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 279–83. http://dx.doi.org/10.55248/gengpi.2023.4.4.34665.

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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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Mohapatra, Alma. "Generative AI for Predictive Maintenance: Predicting Equipment Failures and Optimizing Maintenance Schedules Using AI." International Journal of Scientific Research and Management (IJSRM) 12, no. 11 (November 8, 2024): 1648–72. http://dx.doi.org/10.18535/ijsrm/v12i11.ec03.

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Predictive maintenance has emerged as a transformative approach to managing equipment health, reducing unplanned downtime, and extending asset lifespan. Leveraging advancements in generative artificial intelligence (AI), this paper explores the role of AI-driven predictive maintenance in predicting equipment failures and optimizing maintenance schedules. Traditional maintenance strategies, such as reactive and preventive approaches, often lead to inefficiencies, increased operational costs, and unexpected breakdowns. Predictive maintenance, powered by AI, offers a proactive alternative that not only anticipates failures but also enhances scheduling efficiency, maximizing equipment uptime and reducing maintenance costs. Generative AI models, including techniques such as Generative Adversarial Networks (GANs) and reinforcement learning, have shown immense promise in learning complex patterns from historical data and simulating potential equipment failure scenarios. These AI-driven models can analyze vast and diverse data sources—including sensor readings, maintenance logs, environmental conditions, and historical failures—to provide accurate, real-time insights into equipment health. This paper details the architecture and functioning of generative AI models in predictive maintenance, emphasizing their role in both anomaly detection and failure prediction. A systematic comparison of reactive, preventive, and predictive maintenance is provided, underscoring the unique benefits and challenges of predictive maintenance. We discuss the types of data essential for predictive maintenance and present sample data structures used in model training and deployment. Additionally, this paper demonstrates how generative AI models predict equipment failures by identifying anomalous behaviors before they escalate, enabling preemptive actions. A failure probability model is presented to illustrate how failure risks evolve over time, alongside tables showcasing the critical data points in predictive maintenance. The paper also explores the optimization of maintenance schedules using generative AI, where models simulate and compare different maintenance timing strategies, ultimately minimizing downtime and maximizing productivity. However, we also acknowledge the current limitations of generative AI in this domain, including data privacy concerns, computational intensity, and the challenges of model interpretability for practical implementation. Looking forward, we examine future trends such as the integration of Internet of Things (IoT) devices and the emergence of more sophisticated AI models that will likely enhance predictive maintenance applications. This paper concludes by highlighting the transformative potential of generative AI for predictive maintenance, offering insights for industries seeking to innovate their maintenance practices and achieve superior operational resilience.
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Lu, Bin, David Durocher, and Peter Stemper. "Predictive maintenance techniques." IEEE Industry Applications Magazine 15, no. 6 (November 2009): 52–60. http://dx.doi.org/10.1109/mias.2009.934444.

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This article discusses the importance of PdM for industrial process applications and investigates a number of emerging technologies that enable this approach, including online energy-efficiency evaluation and continuous condition monitoring. The article gives an overview of existing and future technologies that can be used in these areas. Two methods for bearing fault detection and energy-efficiency estimation are discussed. The article concludes with focus on one pilot installation at Weyerhaeuser's Containerboard Packaging Plant in Manitowoc, Wisconsin, USA, monitoring three critical induction motors: a 75-hrho blower motor, a 50-hrho hydraulic pump motor, and a 200-hp compressor motor. Finally, the field experience gained in this plant is presented as two case studies.
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Entek Scientific Corporation. "Predictive maintenance system." NDT & E International 27, no. 3 (June 1994): 172. http://dx.doi.org/10.1016/0963-8695(94)90749-8.

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Emmanuel Augustine Etukudoh. "THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM." Engineering Science & Technology Journal 5, no. 3 (March 24, 2024): 913–23. http://dx.doi.org/10.51594/estj.v5i3.946.

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The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation. Keywords: Predictive Maintenance, System, ECOPFM, Technology.
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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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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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MOHD ALI, AHMAD ALI IMRAN, MD MAHADI HASAN IMRAN, SHAHRIZAN JAMALUDIN, AHMAD FAISAL MOHAMAD AYOB, MOHAMMED ISMAIL RUSSTAM SUHRAB, SYAMIMI MOHD NORZELI, SAIFUL BAHRI HASAN BASRI, and SAIFUL BAHRI MOHAMED. "A REVIEW OF PREDICTIVE MAINTENANCE APPROACHES FOR CORROSION DETECTION AND MAINTENANCE OF MARINE STRUCTURES." JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 19, no. 4 (April 30, 2024): 180–200. http://dx.doi.org/10.46754/jssm.2024.04.014.

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Corrosion is a natural phenomenon that deteriorates and damages the surface of metallic material. Over time, the surface of the material deteriorates due to electrochemical reactions with the surrounding environment. If corrosion is not identified early on, it can become a major financial burden for industries, costing billions of dollars. Despite swift technological developments, preventing and maintaining corrosion progression with reactive maintenance remains difficult. Due to that, predictive maintenance has been developed to predict the deterioration, degradation, and fault over the remaining useful life of the material by using real-time data, historical data, simulation, modelling, and failure probability. Predictive maintenance allows inspectors to monitor the health and predict the corrosion level of the material. However, it is hard to predict the unexpected degradation of the material from the developed prediction model without considering the harsh environment and other external factors. Hence, there is a need to investigate these problems and their effect on predictive maintenance for corrosion detection and maintenance. Therefore, this paper reviews and compares the state-of-the-art predictive maintenance solutions developed to solve corrosion issues in various applications, industries, and academic research. The challenges and opportunities for the predictive maintenance application of corrosion detection and maintenance are also presented. This review will provide new and additional knowledge that can be used to develop prediction models for corrosion detection and maintenance, which will help prevent unexpected failures.
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Segovia-Muñoz, D., X. Serrano-Guerrero, and A. Barragán-Escandon. "Predictive maintenance in LED street lighting controlled with telemanagement system to improve current fault detection procedures using software tools." Renewable Energy and Power Quality Journal 20 (September 2022): 379–86. http://dx.doi.org/10.24084/repqj20.318.

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Predicting the lifetime of LED light sources becomes quite challenging because the time to failure is long. The LM-80 and TM-21 methods are the main used by companies to establish the product lifetime. Accurate the RUL prediction can facilitate predictive maintenance. Predictive maintenance allows estimating when a failure will occur. In this context, the maintenance can be planned in advance, eliminating unplanned outage and maximizing the useful life of the equipment. In this work, the LM-80 and TM-21 methods are used for the acquisition and extrapolation of luminous flux data, wich are entered into an algorithm developed from an exponential degradation model. With the result obtained, it is possible to establish actions that allow predictive maintenance in LED street lighting controlled by a remote management system and achieve a longer service life.
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Dissertations / Theses on the topic "Predictive maintenance"

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Li, Jiawei M. Eng Massachusetts Institute of Technology. "A case model for predictive maintenance." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/43139.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2008.
Includes bibliographical references (leaves 59-60).
This project is to respond to a need by Varian Semiconductor Equipment Associates, Inc. (VSEA) to help predict failure of ion implanters. Predictive maintenance would help to reduce the unscheduled downtime of ion implanters, whose throughput and uptime is highly important to customers. Statistical analysis is performed on historical data to extract metadata that can reflect the machine health, and statistical process control (SPC) is applied to detect deviations from normal or in-control behavior. Methods for failure prevention are also investigated. Challenging points in this project are the noise in raw signal data and the difference in data signals of different robots. To address these challenges, we apply signal filtering to extract cycle motions from raw data, and develop different generic as well as specific metadata extraction techniques for different robots. We test the extraction approaches and results using healthy data of ten machines, and find that the metadata on which we chose to perform SPC is suitable and can serve as a consistent indicator of a machine's health. We further develop an application using Visual Basic based on our study, and provide a user guide on how to generate the analysis reports on new data using our application.
by Jiawei Li.
M.Eng.
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Tyagi, Prakhar. "Chassis predictive maintenance and service solutions." Thesis, KTH, Fordonsdynamik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265587.

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Predictive Maintenance (PdM) accumulates data from multiple sensors developing a statistical model which identifies the key failures even before they take place. The main focus of this thesis work has been the proposal of a machine learning based system designed for predicting the failure of mechanical parts that require replacement. The main investigation explores the possibilities of implementing machine learning algorithm for predicting the parts that require replacement and which is found from the electronic errors that the vehicle exhibits. A strong association between the parts that cause faults and electronic error codes helps in yielding a powerful diagnostics tool. The study has considered three error components namely; broken damper, noisy wheel hub and the reference value for the validation purpose. The model vehicle used for the study is Volvo V90. To acquire variance in this study data, diverse tracks with different speeds were used. The machine learning algorithm that was developed can classify and detect mechanical failures using an Support Vector Machine (SVM) algorithm based on various statistical learning methods. The study carried out an fast Fourier transform (FFT) analysis in association with the data acquired from front left wheel. The main area of interest is the FFT domain of 5-20hz. The study outcome indicated that the used model is capable of predicting the hysteretic responses associated with the faulty components like broken damper and noisy wheel hub. The designed model can be used for analysing the system’s response and for designing and controlling the faulty components in the car. However, the results of this thesis work can be used to implement the time-based prediction of mechanical component decay.
Prediktivt Underhåll (PdM) är en statistisk modell som samlar data från flera olika sensorer och som identifierar fel innan de äger rum. Huvudfokus för detta examensarbete har varit förslaget till ett maskininlärningsbaserat system som är utformat för att förutsäga fel i mekaniska delar som kräver utbyte. Examensarbetet undersöker möjligheterna att implementera en maskininlärningsalgoritm för att förutsäga de mekaniska delar som kräver utbyte och som framgår av de elektroniska fel som fordonet uppvisar. En stark koppling mellan de delar som orsakar fel och elektroniska felkoder hjälper till att ge ett kraftfullt diagnostiskt verktyg. Studien har beaktat tre felkomponenter nämligen; trasig dämpare, missljud från hjulnav och referensvärdet för valideringsändamål. Modellfordonet som används för studien är Volvo V90. För att få varians i informationen för detta arbete användes olika provbanor med olika vägförhållanden med olika hastigheter. Maskininlärningsalgoritmen som utvecklades kan klassificera och upptäcka mekaniska fel med hjälp av en SVM-algoritm (Support Vector Machine) baserad på olika statistiska inlärningsmetoder. Studien genomförde en snabb Fourier-transform (FFT) analys i samband med de data som förvärvades från det främre vänstra hjulet. Huvudintresseområdet är FFT-domänen 5-20 Hz. Studiens resultat visade att den använda modellen kan: Identifiera och klassificera data som är förknippade med de felaktiga komponenterna som trasig dämpare och missljud i hjulnav. Modellen kan användas för vidare prediktera och ge förslag när ett mekaniskt fel på dämpare eller hjulnav håller på att ske. Det här examensarbetet täcker inte tidsbunden prediktion utan snarare identifierar när nedbrytningen av mekaniska komponenter har skett. Resultaten från detta examensarbete kan emellertid användas för att implementera en tidsbaserad prediktion för mekaniska komponentfel.
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Korvesis, Panagiotis. "Machine Learning for Predictive Maintenance in Aviation." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX093/document.

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L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement / transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normal
The increase of available data in almost every domain raises the necessity of employing algorithms for automated data analysis. This necessity is highlighted in predictive maintenance, where the ultimate objective is to predict failures of hardware components by continuously observing their status, in order to plan maintenance actions well in advance. These observations are generated by monitoring systems usually in the form of time series and event logs and cover the lifespan of the corresponding components. Analyzing this history of observation in order to develop predictive models is the main challenge of data driven predictive maintenance.Towards this direction, Machine Learning has become ubiquitous since it provides the means of extracting knowledge from a variety of data sources with the minimum human intervention. The goal of this dissertation is to study and address challenging problems in aviation related to predicting failures of components on-board. The amount of data related to the operation of aircraft is enormous and therefore, scalability is a key requirement in every proposed approach.This dissertation is divided in three main parts that correspond to the different data sources that we encountered during our work. In the first part, we targeted the problem of predicting system failures, given the history of Post Flight Reports. We proposed a regression-based approach preceded by a meticulous formulation and data pre-processing/transformation. Our method approximates the risk of failure with a scalable solution, deployed in a cluster environment both in training and testing. To our knowledge, there is no available method for tackling this problem until the time this thesis was written.The second part consists analyzing logbook data, which consist of text describing aircraft issues and the corresponding maintenance actions and it is written by maintenance engineers. The logbook contains information that is not reflected in the post-flight reports and it is very essential in several applications, including failure prediction. However, since the logbook contains text written by humans, it contains a lot of noise that needs to be removed in order to extract useful information. We tackled this problem by proposing an approach based on vector representations of words (or word embeddings). Our approach exploits semantic similarities of words, learned by neural networks that generated the vector representations, in order to identify and correct spelling mistakes and abbreviations. Finally, important keywords are extracted using Part of Speech Tagging.In the third part, we tackled the problem of assessing the health of components on-board using sensor measurements. In the cases under consideration, the condition of the component is assessed by the magnitude of the sensor's fluctuation and a monotonically increasing trend. In our approach, we formulated a time series decomposition problem in order to separate the fluctuation from the trend by solving a convex program. To quantify the condition of the component, we compute a risk function which measures the sensor's deviation from it's normal behavior, which is learned using Gaussian Mixture Models
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Karlsson, Lotta. "Predictive Maintenance for RM12 with Machine Learning." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42283.

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Few components within mechanical engineering possess the fatigue resistance as of high-pressure turbine blades found in jet engines. This as they are designed to perform in extensively high temperatures under severe loading which causes degradation to be an important aspect despite a design, optimized for its environment. This study aims to find a method for predicting life consumption of those blades belonging to the turbine section of the jet engine in JAS 39 Gripen C/D called RM12. This was performed at GKN Aerospace, which holds the military type certificate for this engine as well as a patented solution that determines life consumption in components depending on operational history. With the help of machine learning in Matlab, flight sensor data and loading results, the method was to explore a variety of prediction models and find a selection of blades with varied utilization before reaching end of life for comparison. Followed by a search of understanding the life limiting fatigue conditions and the factors involved in the deterioration process. A similarity finding approach gave valuable meaning to the accuracy of regression analysis from flight data towards output in form of temperature predictions. Comparing known and reliable fatigue calculation results gave however no clear picture as inspected blades had reach their limit at very diverse accumulated values. The next approach was therefore to investigate if an initialization point of degradation could be found, from where the result could give an answer that matched for all blades and their different utilization. The result was that an accelerated degradation after high loading could give a prediction that could explain the total life consumption with an accuracy of 87% for 19 out of 21 investigated blades. The accelerated deterioration could in theory be explained by the fact that the fatigue resistance as well as different types of degradation, propagates each other and originates from thermal loading making them all contributors, whereas the conventional numerical methods only handles them separately. In order to get confidence, valuable and reliable predictions, the models do however need to be accompanied with more testing and adding of contributing factors before assumed as a proven method for life consumption determination of the high-pressure turbine blades.
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Sedghi, Mahdieh. "Data-driven predictive maintenance planning and scheduling." Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.

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The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in severe cases, can cause severe accidents such as derailments. An effective maintenance management system is required to ensure the availability of the infrastructure system and meet the increasing capacity demand. The recent rapid technological revolution and increasing deployment of sensors and connected devices created new possibilities to increase the maintenance strategy effectiveness in the railway network. The purpose of this thesis is to expand the knowledge and methods for planning and scheduling of railway infrastructure maintenance. The research vision is to find quantitative approaches for integrated tactical planning and operational scheduling of predictive condition-based maintenance which can be put to practical use and improve the efficiency of the railway system. First, a thorough literature review study is performed to identify improvement policies for maintenance planning and scheduling in the literature and also to analyse the current approaches in optimising the maintenance planning and scheduling problem. Second, a novel data-driven multi-level decision-making framework to improve the efficiency of maintenance planning and scheduling is developed. The proposed framework aims to support the selection of track segments for maintenance by providing a practical degradation prediction model based on available condition measurement data. The framework considers the uncertainty of future predictions using the probability of surpassing a maintenance limit instead of using the predicted value. Moreover, an extensive total maintenance cost formulation is developed to include both direct and indirect preventive and corrective costs to observe the effect of using cost optimisation and grouping algorithms at the operational scheduling level. The performance of the proposed framework is evaluated through a case study based on data from a track section of the iron ore line between Boden and Luleå. The results indicate that the proposed approach can lead to cost savings in both optimal and grouping plans. This framework may be a useful decision support tool in the automated planning and scheduling of maintenance based on track geometry measurements.
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Killeen, Patrick. "Knowledge-Based Predictive Maintenance for Fleet Management." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40086.

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In recent years, advances in information technology have led to an increasing number of devices (or things) being connected to the internet; the resulting data can be used by applications to acquire new knowledge. The Internet of Things (IoT) (a network of computing devices that have the ability to interact with their environment without requiring user interaction) and big data (a field that deals with the exponentially increasing rate of data creation, which is a challenge for the cloud in its current state and for standard data analysis technologies) have become hot topics. With all this data being produced, new applications such as predictive maintenance are possible. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life, which could help companies lower their fleet management costs by reducing their fleet's average vehicle downtime. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN). The VN represents the vehicle and performs lightweight data acquisition, data analytics, and data storage. The VN is connected to the fleet via its wireless internet connection. The SLN is responsible for managing a region of vehicles, and it performs more heavy-duty data storage, fleet-wide analytics, and networking. The RN is the central point of administration for the entire system. It controls the entire fleet and provides the application interface to the fleet system. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a garage of the Soci\'et\'e de Transport de l'Outaouais (STO), Gatineau, Canada. The VN in the MVP was implemented using a Raspberry Pi, which acquired sensor data from a STO hybrid bus by reading from a J1939 network, the SLN was implemented using a laptop, and the RN was deployed using meshcentral.com. The goal of the MVP was to perform predictive maintenance for the STO to help reduce their fleet management costs. The present work also proposes a fleet-wide unsupervised dynamic sensor selection algorithm, which attempts to improve the sensor selection performed by the COSMO approach. I named this algorithm the improved consensus self-organized models (ICOSMO) approach. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a STO hybrid bus, which was acquired using the MVP, was used to generate synthetic data to simulate vehicles, faults, and repairs. The deviation detection of the COSMO and ICOSMO approach was applied to the synthetic sensor data. The simulation results were used to compare the performance of the COSMO and ICOSMO approach. Results revealed that in general ICOSMO improved the accuracy of COSMO when COSMO was not performing optimally; that is, in the following situations: a) when the histogram distance chosen by COSMO was a poor choice, b) in an environment with relatively high sensor white noise, and c) when COSMO selected poor sensors. On average ICOSMO only rarely reduced the accuracy of COSMO, which is promising since it suggests deploying ICOSMO as a predictive maintenance system should perform just as well or better than COSMO . More experiments are required to better understand the performance of ICOSMO. The goal is to eventually deploy ICOSMO to the MVP.
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Williamsson, Ia. "Total Quality Maintenance (TQMain) A predictive and proactive maintenance concept for software." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2281.

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This thesis describes an investigation of the possibility to apply a maintenance concept originally developed for the industry, on software maintenance. Today a large amount of software development models exist but not many of them treat maintenance as a part of the software life cycle. In most cases maintenance is depicted as an activity towards the end of the software life cycle. The high cost ascribed to software maintenance motivates for improvements. The maintenance concept TQMain proposed in this thesis distinguishes from other maintenance concepts by its use of preventive, predictive and proactive maintenance strategies. TQMain uses a common database to store real-time data from various departments and uses it for analyse and assessment to track the development of deviations in the condition of the production process and product quality at an early stage. A continuous cyclic improvement of the maintenance strategy is reached by comparing the data from the real-time measurements with data from the database. The ISO/IEC Software engineering – Product qualities is used as a source of empiric data to conclude that the correct quality characteristics are used for identifying software product quality and its characteristics and compare them with the characteristics of industrial product quality. The results presented are that in the conceptual outline of TQMain measures are obviously not the same as in software maintenance, but the aspect of product quality is common for both. The continuous cyclic improvement of the product quality that TQMain features together with the aspect of detecting potential failures before they occur would, judging from the conceptual outline of TQMain be applicable on software maintenance.
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Pryor, Jacqueline. "Earthwork maintenance : a geotechnical database and predictive model." Thesis, Cardiff University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266614.

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De, Giorgi Marcello. "Tree ensemble methods for Predictive Maintenance: a case study." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22282/.

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Nel lavoro descritto in questa tesi sono stati creati modelli per la manutenzione predittiva di macchine utensili in ambito industriale; in particolare, i modelli realizzati sono stati addestrati sfruttando degli ensemble tree methods con le finalità di: predire il verificarsi di un guasto in macchina con un anticipo tale da permettere l'organizzazione delle squadre di manutenzione; predire la necessità della sostituzione anticipata dell'utensile utilizzato dalla macchina, per mantenere alti gli standard di qualità. Dopo aver dato uno sfondo al contesto industriale in esame, la tesi illustra i processi seguiti per la creazione e l'aggregazione di un dataset, e l'introduzione di informazioni relative agli eventi in macchina. Analizzato il comportamento di alcune variabili durante la lavorazione ed effettuata una distinzione tra cicli di lavorazione validi e non validi, si procede introducendo gli ensemble tree methods e il motivo della scelta di questa classe di algoritmi. Nel dettaglio, vengono presentati due possibili candidati al problema trattato: Random Forest ed XGBoost; dopo averne descritto il funzionamento, vengono presentati i risultati ottenuti dai modelli proponendo, per stimarne l'efficacia, un funzione di costo atteso come alternativa all'accuracy score. I risultati dei modelli allenati con i due algoritmi proposti vengono infine confrontati.
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FURTADO, FELIPE MIANA DE FARIA. "NEURAL NETWORKS FOR PREDICTIVE MAINTENANCE ON OFF-HIGWAY TRUCKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=15673@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Com o aumento da demanda por minério no mundo, a complexidade, o tamanho e o preço dos equipamentos de extração mineral aumentaram consideravelmente. Como estas máquinas possuem uma tecnologia de monitoramento embarcada no equipamento, a utilização desses dados para o aumento da confiabilidade e da disponibilidade do equipamento tornou-se fundamental, de modo a reduzir os custos de manutenção. O objetivo desta dissertação foi desenvolver um modelo de apoio à decisão de parada de equipamento, baseado na classificação por Redes Neurais Artificiais de padrões pré-falha de caminhões fora de estrada. O modelo proposto tem como objetivo identificar o estado de falha, ou padrão pré-falha de um equipamento, utilizando os dados armazenados nos equipamentos e seus respectivos registros de falha, para que seja possível avaliar o risco de falha deste equipamento e decidir se o mesmo deve ser parado ou aguardar uma nova parada programada. Essa dissertação foi desenvolvida em quatro partes: estudo dos principais modelos de manutenção atualmente utilizados; definição e desenvolvimento do modelo para abordar o problema, baseado em redes neurais artificiais; avaliação de desempenho do modelo proposto; e simulação do downtime da máquina utilizando o modelo de decisão proposto. No estudo dos principais modelos foi realizada uma pesquisa bibliográfica sobre a evolução da manutenção, passando por modelos de manutenção corretiva, manutenção preventiva e, por fim, chegando ao modelo de manutenção baseada no monitoramento de condições. Para os dois últimos tipos de manutenção, foram apresentados os principais modelos utilizados na abordagem do problema, seus benefícios e deficiências. O desenvolvimento do modelo foi segmentado em três etapas principais: tratamento das bases de dados, tanto de dados obtidos diretamente do equipamento quanto das bases de registro de falha dos equipamentos; seleção de variáveis, baseada no cálculo da influência de cada sensor do equipamento na determinação de seu estado de falha, assim como na definição do intervalo ideal para se agrupar os dados; e definição da topologia das redes. Na etapa de avaliação do desempenho do modelo proposto foram utilizados dados de falhas corretivas mais recorrentes para os dois componentes específicos de caminhões fora de estrada: motor e transmissão, sendo que o monitoramento eletrônico do motor é mais extenso do que o de transmissão, no que diz respeito ao número de sensores empregados no monitoramento. Para a comparação de desempenho entre os diferentes modelos avaliados, dois fatores tiveram maior relevância: melhor desempenho na classificação e maior intervalo entre a identificação do padrão pré-falha e a ocorrência da falha. Os resultados de classificação dos padrões pré-falha foram bastante satisfatórios para a maioria dos casos de estudos, com as taxas de acerto variando entre 85% e 95%. A partir do modelo de classificação determinado na etapa anterior, passou-se à simulação de diferentes cenários de falhas, calculando-se os tempos de máquina parada (downtimes) que teriam sido evitados se as intervenções definidas pelo modelo tivessem sido executadas, analisando-se, assim, o aumento de disponibilidade proporcionado pelo uso do modelo proposto.
With the increasing demand for ore in the world, the complexity, size and price of mining equipment have increased considerably. As these machines have embedded monitoring technology, the use of such data to increase the reliability and availability of the equipment has become essential in order to reduce maintenance costs. The objective of this work is developing a model that supports the decision of stopping an equipment, based on its actual state, using pattern recognition by neural networks. The proposed model aims to identify the state of equipment failure or pre-failure based on the data stored in the equipment and on the records of failure, so as to assess the risk of failure of equipment and to decide whether it should be stopped or wait for a new programmed shutdown. This dissertation was developed in four parts: study of the main models currently used for maintenance; design and implementation of the model to address this problem, based on artificial neural networks; performance evaluation of the proposed model; and simulation of equipment downtime using the proposed model. In the study of the main models a research was made about the evolution of maintenance techniques, through models of corrective maintenance, preventive maintenance and, finally, reaching the maintenance model based on condition monitoring. For the last two types of maintenance, it is presented the main models used in addressing the problem, its benefits and shortcomings. The development of the model was segmented into three main stages: processing of databases, from the data obtained directly from the equipment to the base of record of equipment failure; variable selection, based on the calculation of the influence of each equipment sensor to determine its failure state, as well as the definition of the ideal range of group data, and definition of the topology of networks. In the stage of assessing the performance of the proposed model we used data from corrective failures more often of two specific components of off-highway trucks: engine and transmission. To compare the performance between the different models evaluated, two factors were more important: classification performance and the longest interval between the identification of a pre-failure pattern and the occurrence of the failure. The results of classification of pre-failure patterns were quite satisfactory for most case studies, with hit rates ranging between 85% and 96%. From the classification model given in the previous step, we moved on to simulate different failure scenarios, calculating the equipment downtime that would have been avoided if the interventions defined by the model had been implemented, thus analyzing the increased availability provided by the use of the proposed model.
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Books on the topic "Predictive maintenance"

1

Liu, Min, Ling Li, and Feng Yan. Intelligent Predictive Maintenance. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6.

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Salter, Richard G. Predictive maintenance and logistics: (PML). Santa Monica, CA: Rand, 1985.

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

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Mobley, R. Keith. An introduction to predictive maintenance. 2nd ed. Amsterdam: Butterworth-Heinemann, 2002.

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C, Scheffer, ed. Practical machinery vibration analysis and predictive maintenance. Amsterdam: Elsevier, 2004.

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Gupta, Shweta. Cognitive Predictive Maintenance Tools for Brain Diseases. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003245346.

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Cash, Carl G. Predictive service life tests for roofing membranes. Champaign, Ill: US Army Corps of Engineers, Construction Engineering Research Laboratories, 1994.

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Ralph, Moshage, and Construction Engineering Research Laboratories (U.S.), eds. Vibration monitoring for predictive maintenance in central energy plants. [Champaign, IL]: US Army Corps of Engineers, Construction Engineering Research Laboratories, 1993.

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Gouriveau, Rafael, Kamal Medjaher, and Noureddine Zerhouni. From Prognostics and Health Systems Management to Predictive Maintenance 1. Hoboken, NJ, USA: 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. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119436805.

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Book chapters on the topic "Predictive maintenance"

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Sharanya, S., Revathi Venkataraman, and G. Murali. "Predictive Maintenance." In Introduction to AI Techniques for Renewable Energy Systems, 155–70. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003104445-10.

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Mahalle, Parikshit N., Pravin P. Hujare, and Gitanjali Rahul Shinde. "Predictive Maintenance." In Predictive Analytics for Mechanical Engineering: A Beginners Guide, 51–60. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4850-5_4.

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Sierra, Carlos, and Emilio Andrea. "Predictive Maintenance Techniques." In Mining Maintenance, 257–78. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59450-2_9.

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Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Diagnosis Methods." In Intelligent Predictive Maintenance, 203–31. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_7.

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Liu, Min, Ling Li, and Feng Yan. "Protocol Integration and Design Case of Data Collection." In Intelligent Predictive Maintenance, 177–201. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_6.

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Liu, Min, Ling Li, and Feng Yan. "Large-Scale Maintenance Service Forecasting and Optimization Configuration." In Intelligent Predictive Maintenance, 325–421. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_10.

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Liu, Min, Ling Li, and Feng Yan. "Wireless Routing Model and Algorithm for Complex Manufacturing Environment." In Intelligent Predictive Maintenance, 153–76. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_5.

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Liu, Min, Ling Li, and Feng Yan. "Operation Process Control Based on Cyber-Physical Systems." In Intelligent Predictive Maintenance, 423–68. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_11.

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Liu, Min, Ling Li, and Feng Yan. "Maintenance Optimization Scheduling and Decision Making in Intelligent Factories." In Intelligent Predictive Maintenance, 281–323. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_9.

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Liu, Min, Ling Li, and Feng Yan. "Introduction." In Intelligent Predictive Maintenance, 1–45. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_1.

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Conference papers on the topic "Predictive maintenance"

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Benešová, Andrea, Martin Hirman, František Steiner, and Jiří Tupa. "Digital Predictive Maintenance: Case Study." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 01–06. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693912.

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Sunetcioglu, Selin, and Taner Arsan. "Predictive Maintenance Analysis for Industries." In 2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 344–47. IEEE, 2024. http://dx.doi.org/10.1109/blackseacom61746.2024.10646292.

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Supramaniam, Aravind, Sharifah Sakinah Syed Ahmad, and Zeratul Izzah Mohd Yusoh. "Predictive Maintenance using Deep Reinforcement Learning." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 671–76. IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10730350.

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Rayhana, Rakiba, Hongguang Yun, Teng Wang, Johnson Chen, Yanshuo Fan, Zheng Liu, and Wendy Gao. "Distributed Predictive Maintenance through Edge Computing." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), 1–13. IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774467.

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Mishra, KamalaKanta, and Sachin Kumar Manjhi. "Failure Prediction Model for Predictive Maintenance." In 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2018. http://dx.doi.org/10.1109/ccem.2018.00019.

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Motaghare, Omkar, Anju S. Pillai, and K. I. Ramachandran. "Predictive Maintenance Architecture." In 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2018. http://dx.doi.org/10.1109/iccic.2018.8782406.

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Liu, Zheng, Erik Blasch, Min Liao, Chunsheng Yang, Kazuhiko Tsukada, and Norbert Meyendorf. "Digital twin for predictive maintenance." In NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, edited by Norbert G. Meyendorf, Ripi Singh, and Christopher Niezrecki. SPIE, 2023. http://dx.doi.org/10.1117/12.2660270.

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Edouard, Thomas,. "Opportune Maintenance and Predictive Maintenance Decision Support." In Information Control Problems in Manufacturing, edited by Bakhtadze, Natalia, chair Dolgui, Alexandre and Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00266.

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Sipos, Ruben, Dmitriy Fradkin, Fabian Moerchen, and Zhuang Wang. "Log-based predictive maintenance." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623340.

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Gama, Joao, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, and Bruno Veloso. "XAI for Predictive Maintenance." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599578.

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Reports on the topic "Predictive maintenance"

1

Church, Joshua, LaKenya Walker, and Amy Bednar. JAIC Predictive Maintenance Dashboard user manual. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41823.

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This manual is intended for new users with minimal or no experience with using the JAIC Predictive Maintenance Dashboard (JPD). The goal of this document is to give an overview of the main functions of JPD. The primary focus of this document is to demonstrate functionality. Every effort has been made to ensure this document is an accurate representation of the functionality of the JPD. For additional information about this manual, contact ERDC.JAIC@erdc.dren.mil.
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Ramasahayam, Uditsena Reddy. Revolutionizing aircraft maintenance: The role of predictive maintenance in aviation. Ames (Iowa): Iowa State University, December 2023. http://dx.doi.org/10.31274/cc-20240624-1236.

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Walker, Cody, Vivek Agarwal, Linyu Lin, Anna Hall, Rachael Hill, Ronald Boring PhD, Torrey Mortenson, and Nancy Lybeck. Explainable Artificial Intelligence Technology for Predictive Maintenance. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1998555.

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Foster, Michelle. Vibration Analysis - Presented to the MMWG Predictive Maintenance User’s Group. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1996132.

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Tsunokai, Manabu. Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada627305.

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Foster, Michelle. Infrared Thermography Applications Presented to the MMWG Predictive Maintenance User’s Group. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1890960.

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Agarwal, Vivek. Identification of Balance of Plant Asset and Wireless Instrumentation to Enable Predictive Maintenance. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/2448467.

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Walker, Cody, Linyu Lin, Vivek Agarwal, Nancy Lybeck, Anna Hall, Rachael Hill, and Ronald Boring PhD. Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance. Office of Scientific and Technical Information (OSTI), September 2024. http://dx.doi.org/10.2172/2474859.

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Hamilton, Jason. Early-Stage Transition to Predictive Maintenance: Using CMMS, IR Scans, and Vibration Analysis to Improve Uptime and Lower Maintenance Costs. Portland State University Library, January 2015. http://dx.doi.org/10.15760/honors.188.

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Agarwal, Vivek, and Andrei Gribok. Markov Process to Evaluate the Value Proposition of a Risk-Informed Predictive Maintenance Strategy. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/2448464.

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