Academic literature on the topic 'Predictive maintenance'

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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 (2023): 279–83. http://dx.doi.org/10.55248/gengpi.2023.4.4.34665.

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Lim, Seunghun, Jungmo Oh, and Jinkyu Park. "Maintenance Time Prediction for Predictive Maintenance of Ship Engines." Applied Sciences 15, no. 9 (2025): 4764. https://doi.org/10.3390/app15094764.

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Ships carrying large amounts of cargo and passengers are larger and slower than other modes of transportation. They are mostly foreign flagged and operate at sea far from coasts for 20 years or more, incurring more operating costs than construction costs. Therefore, an efficient maintenance system is necessary for stable, economical ship operation. Researchers are attempting to equip ships with predictive maintenance technology, which is used proactively in other modes of transportation to predict the maintenance time of machines through data monitoring and analysis. However, due to the nature
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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 possi
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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 (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 no
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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
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Mohamed Abdul Kadar Mohamed Jabarullah. "PredictNet: AI-enabled predictive maintenance system for telecommunications infrastructure reliability." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 631–39. https://doi.org/10.30574/wjarr.2022.15.3.0954.

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This paper introduces PredictNet, a novel AI-enabled predictive maintenance system designed specifically for telecommunications infrastructure. The research addresses the critical challenge of maintaining reliability in increasingly complex telecom networks while reducing operational costs. Using machine learning algorithms and real-time sensor data, PredictNet demonstrates superior performance in predicting equipment failures before they occur. The system was implemented and tested on a mid-sized telecommunications network over a 12-month period, achieving 92.7% prediction accuracy with a mea
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Lu, Bin, David Durocher, and Peter Stemper. "Predictive maintenance techniques." IEEE Industry Applications Magazine 15, no. 6 (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 inducti
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Entek Scientific Corporation. "Predictive maintenance system." NDT & E International 27, no. 3 (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 (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 en
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Dr, Amol Ubale Lata Kamble Ganesh Ghodnadikar Mohhamad Zaid Ali Khan Apurva Khamkar. "Development of an Android /IOS/ Microsoft App for Industrial Task Management and Record Storages." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 179–84. https://doi.org/10.5281/zenodo.12596049.

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Optimizing mechanical productivity is crucial for maintaining efficiency and competitiveness in industrial operations. This paper addresses the challenges faced by Atharva Thermal Energy, including inefficient maintenance practices, limited predictive maintenance capabilities, and communication barriers. To mitigate these issues, a comprehensive mechanical optimization initiative is proposed, incorporating predictive and breakdown maintenance strategies within a structured hierarchical framework. The initiative aims to integrate predictive maintenance, streamline breakdown maintenance procedur
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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.<br>Includes bibliographical references (leaves 59-60).<br>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) i
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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 caus
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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
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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 pa
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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 sever
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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 dat
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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 an
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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
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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<br>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 Neu
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Books on the topic "Predictive maintenance"

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Liu, Min, Ling Li, and Feng Yan. Intelligent Predictive Maintenance. 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). Rand, 1985.

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

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

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Gupta, Shweta. Cognitive Predictive Maintenance Tools for Brain Diseases. 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. 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. 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. 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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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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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). 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). IEEE, 2024. http://dx.doi.org/10.1109/blackseacom61746.2024.10646292.

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Rayhana, Rakiba, Hongguang Yun, Teng Wang, et al. "Distributed Predictive Maintenance through Edge Computing." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774467.

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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). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10730350.

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Fischer, Paula, and Martin Dazer. "Evaluating Success Conditions for Predictive Maintenance." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935273.

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Maillard, Pierre-François, Sudipta Das, and Bimal Kumar Roy. "Minimal Adversarial Attacks on Predictive Maintenance." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927430.

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S, Sridevi, Jayajevaa N, Deeksha S. R, R. Ramya, Nitin Sriram M, and Jacob M. "Predictive Maintenance System for CNC Machines." In 2025 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC). IEEE, 2025. https://doi.org/10.1109/iceccc65144.2025.11064291.

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Meyer, Fred H., and Robert N. Miller. "Optimizing Aircraft Maintenance by Predictive Corrosion Modeling." In CORROSION 1987. NACE International, 1987. https://doi.org/10.5006/c1987-87226.

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Abstract A progress report is presented for a program to develop a predictive corrosion model which will enable optimum inspection and maintenance scheduling for Air Force planes. The model is based on the kinetics of corrosion reactions, the degradation rates of protective coating systems and the environmental conditions existing at various Air Force bases. Corrosion rates for 7075-T6 and 7075-T73 aluminum and 4340 and 300M steel in solutions containing varying amounts of atmospheric pollutants were determined. A procedure was developed for predicting crack growth on aircraft stationed in a v
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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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Reports on the topic "Predictive maintenance"

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Church, Joshua, LaKenya Walker, and Amy Bednar. JAIC Predictive Maintenance Dashboard user manual. Engineer Research and Development Center (U.S.), 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. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-1236.

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Walker, Cody, Vivek Agarwal, Linyu Lin, et al. Explainable Artificial Intelligence Technology for Predictive Maintenance. Office of Scientific and Technical Information (OSTI), 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), 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. Defense Technical Information Center, 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), 2022. http://dx.doi.org/10.2172/1890960.

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Iyer, Naresh, and Abhinav Saxena. PMDT: AI-Enabled Predictive Maintenance Digital Twins for Advanced Nuclear Reactors. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2527308.

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

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Walker, Cody, Linyu Lin, Vivek Agarwal, et al. Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2474859.

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