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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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ANDRIOAIA, DRAGOS-ALEXANDRU, and VASILE GHEORGHITA GAITAN. "A SPECIALTY LITERATURE REVIEW OF THE PREDICTIVE MAINTENANCE SYSTEMS." Journal of Engineering Studies and Research 29, no. 4 (2024): 17–23. http://dx.doi.org/10.29081/jesr.v29i4.002.
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Within the industrial standard 4.0, predictive maintenance has an essential role in production activities, by increasing equipment uptime and decreasing maintenance costs. Predictive maintenance monitors assets through sensors for optimal planning of maintenance operations to keep assets functional. In this paper, the authors perform an analysis of predictive maintenance system, proposed in the specialized literature, highlighting their component elements.
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Panov, Stefan, Anton Nikolov, and Svetlana Panova. "Review of standards and systems for predictive maintenance." Science, Engineering and Education 6, no. 1 (2021): 65–73. http://dx.doi.org/10.59957/see.v6.i1.2020.1.
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This paper aimed to the analysis of the systems for predictive maintenance. It discusses the methods for maintenance of the industrial equipment and facilities, as well as the requirements and standards set for the predictive maintenance software. It compares the capabilities of the most popular non- commercial and commercial software systems and platforms for predictive maintenance as OSA-CBR, PHM, MIMOSA, PLM, CMMS, etc.
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Pasupuleti, Shashank. "Using Digital Twins for Fault Detection and Root Cause Analysis in Mechanical Systems." International Scientific Journal of Engineering and Management 04, no. 01 (2025): 1–6. https://doi.org/10.55041/isjem01640.
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The integration of Digital Twin (DT) technology into mechanical systems has shown significant potential for enhancing fault detection, diagnostics, and root cause analysis. By creating real-time virtual replicas of physical systems, DTs facilitate continuous monitoring and provide actionable insights into system behavior. This paper explores the application of DT technology in mechanical systems, focusing on its role in fault prevention, predictive maintenance, and root cause analysis. We investigate key aspects such as real-time data synchronization, predictive maintenance strategies, system optimization, and the use of multi-sensor integration to improve fault detection accuracy. The paper also examines the challenges associated with implementing DTs in complex mechanical systems and discusses future directions for research in this field. By leveraging machine learning and advanced data fusion techniques, Digital Twins enable predictive analytics, improving system reliability, efficiency, and overall performance. This work highlights how DTs can transform traditional maintenance strategies, leading to more proactive, data-driven approaches for fault detection and system recovery. Keywords: Digital Twin, fault detection, root cause analysis, mechanical systems, predictive maintenance, real- time data synchronization, system optimization, fault prevention, machine learning, predictive models, sensor data, system performance, anomaly detection, vibration analysis, remaining useful life (RUL), fault recovery, predictive analytics, system reliability, maintenance strategy, real-time monitoring, virtual model, operational efficiency, mechanical failure, sensor fusion, failure prediction, condition-based maintenance, fault detection algorithms, system behavior simulation, data-driven decision making, root cause identification, industrial applications.
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Hu, Huan, Kang Xu, Xianya Zhang, et al. "Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures." Electronics 14, no. 3 (2025): 625. https://doi.org/10.3390/electronics14030625.
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The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive maintenance emerges as a promising technique for accurately forecasting faults. In this study, we investigated the diagnosis and prediction of fault states, specifically single-phase short circuit (1HCF) and double-phase short circuit (2HCF) faults, using monitoring data from current transformers in 110 kV substations. We proposed a predictive maintenance method for current transformers based on core-type structures, which integrates wavelet transform to extract multi-level frequency domain features, employs feature selection techniques (including the Spearman correlation coefficient and mutual information) to identify key predictive features, and utilizes Random Forest classifiers for fault state prediction. Experimental results demonstrate an overall prediction accuracy of 94%.
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Korolev, Vyacheslav I. "METHODS OF PREDICTIVE MONITORING OF THE TECHNICAL CONDITION OF ELECTRICAL SYSTEMS." ELECTRICAL AND DATA PROCESSING FACILITIES AND SYSTEMS 19, no. 2 (2023): 62–72. http://dx.doi.org/10.17122/1999-5458-2023-19-2-62-72.
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Relevance During operation of electromechanical machines, their specifications may change, which can lead to machine failures. Like all electromechanical equipment, rotating machinery is subject to many different adverse effects, such as thermal and environmental stresses and mechanical damage, which require the utmost attention. In industry, any machine or system failure or unplanned downtime can degrade or interrupt a company's core business, potentially resulting in significant fines and immeasurable loss of reputation. Existing traditional approaches to maintenance (maintenance by failure or regulation) suffer from some assumptions and limitations, such as high prevention or repair costs, inadequate or inaccurate mathematical degradation processes. Due to the trend toward smart manufacturing, data mining, and artificial intelligence, predictive maintenance is proposed as a new type of maintenance only after analytical models predict certain failures or degradations. Modern systems for assessing the technical condition of electromechanical equipment are decision support systems based on machine learning. Aim of research The aim of this research is to provide a general overview of maintenance goals and objectives, which mainly include cost minimization, availability/ reliability maximization, and multicriteria optimization. In addition, an overview of existing approaches for fault diagnosis and prediction in predictive maintenance systems is proposed, which include two main subcategories: knowledge-based approaches and traditional machine learning methods. Research methods Currently, many methods based on processing information from measurement transducers, which use acoustic and vibration sensors, current and voltage sensors, temperature sensors, and electromagnetic transducers, using machine learning techniques, have been developed for fault diagnosis and failure time prediction of electromechanical units. Results As a result, the paper presents a brief overview of the aims and approaches of a predictive maintenance system for electromechanical systems based on machine learning techniques used in various electromechanical equipment. An overview of various predictive maintenance methods is presented. An overview of existing approaches based on machine learning is given.
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Andrushko, Andriy, Mykhaylo Lobur, and Marek Iwaniec. "Predictive maintenance – a major field for the application of computer aided systems." Computer Design Systems. Theory and Practice 4, no. 1 (2022): 49–56. http://dx.doi.org/10.23939/cds2022.01.049.
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Predictive maintenance is a widely applied maintenance program that requires extensive support of computer aided systems. The program uses specific procedures that are to be addressed when developing predictive maintenance software solutions. Despite the fact that software solutions for predictive maintenance were introduced almost at the same time as the program emerged, it still remains a very actual field for the application of computer aided systems. The practice also shows that developers of computer aided systems for predictive maintenance constantly encounter problems, trying to translate predictive maintenance procedures into the computer language. These procedures are very specific and require microprocessor-based equipment and development of sophisticated algorithms. Therefore, there is a distinct need for better awareness about the predictive maintenance concept among software developers. The article aims to describe the essence of the predictive maintenance concept, its fundamental approaches and the main physical processes upon which the predictive maintenance procedures are based: (1) distinct vibration frequency components which are inherent in all common failure modes; and (2) constant amplitude of each distinct vibration component. The importance of the awareness with the concept for computer aided systems developers is emphasized. And the most problematic areas of software application in predictive maintenance programs are outlined, namely the development of effective computerized systems to capture and analyze an immense quantity of data (big data processing), and the development of systems, supporting an intelligent connection of smart devices with the means of internet protocols (Internet of Things).
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Saurabh, Saurabh. "Comparative Study of Machine Learning Algorithms in Predicting Load-Induced Bridge Failures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48074.
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Abstract: Bridges are critical components of transportation infrastructure, and their failure can lead to severe economic losses and safety risks. Traditional methods of monitoring and predicting structural failures often rely on manual inspections and periodic maintenance, which may miss early warning signs of degradation. This research explores the application of Artificial Intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting structural failures of bridges. By analyzing data from sensors embedded in bridge structures, such as strain gauges, accelerometers, and displacement transducers, AI algorithms can detect patterns indicative of early damage, such as fatigue, corrosion, and structural weaknesses. The study focuses on developing predictive models using historical data on bridge failures, structural health monitoring (SHM) systems, and real-time data from Internet of Things (IoT) devices. The results demonstrate that AI-based predictive maintenance can significantly enhance the accuracy of failure prediction, reduce inspection costs, and improve bridge safety. This research highlights the potential of AI to transform bridge monitoring systems, making them smarter, more proactive, and capable of addressing the challenges of aging infrastructure. Keywords: Artificial Intelligence, Structural Failures, Bridges, Machine Learning, Structural Health Monitoring, IoT, Predictive Maintenance.
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., Murugadass, P. Sheela Gowr, M. Latha, and U. V. Anbazhagu. "IOT connected predictive vehicle systems." International Journal of Engineering & Technology 7, no. 2.21 (2018): 391. http://dx.doi.org/10.14419/ijet.v7i2.21.12449.
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Predictive maintenance is to identify vehicle maintenance issues before they occur. By leveraging data from navigation locator and motion of vehicle, status and parts of the vehicle, requirement of service, warranty repairs with current vehicle sensor data would be difficult for a human to discover. Predictive data analytics can find meaningful correlations via Connected Vehicle which is a technological advancement in Automobile industry. Using Internet of Things IOT, various information like health information of a driving person and navigation of vehicle can be easily monitored. Connected vehicle deals with cars and other vehicles where we the data will be shared with the backed applications like micro services.
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Nentwich, Corbinian, Maximilian Benker, Johannes Ellinger, et al. "Predictive Maintenance in der Produktion/Predictive Maintenance within the industrial value chain." wt Werkstattstechnik online 110, no. 03 (2020): 98–102. http://dx.doi.org/10.37544/1436-4980-2020-03-14.
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Ungeplante Maschinenausfälle führen zu Stillständen in der Produktion, die große Auswirkungen auf die Wertschöpfungskette eines Unternehmens haben. Der Einsatz von Predictive Maintenance (PdM) entlang dieser Kette erhöht die Maschinenverfügbarkeit und sichert einen reibungslosen Produktionsablauf. Im Rahmen verschiedener Forschungsprojekte am iwb werden Anwendungsfälle für PdM in der Fertigung, der Montage und der Produktionssteuerung betrachtet. Dieser Beitrag beleuchtet individuelle Herausforderungen, Lösungsansätze und Grenzen von PdM im produktionstechnischen Umfeld.   Unplanned machine downtimes can have a big impact on the value chain of a company. Predictive Maintenance (PdM) shows the potential to increase machine availability and secures smooth production processes. Different research projects at iwb examine applications of PdM within manufacturing and assembly, as well as the integration of such approaches in production planning. This article highlights individual challenges, solutions and limits of PdM within production.
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Rodrigues, Joao, Jose Torres Farinha, and Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (January 22, 2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.
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The importance given to the maintenance in the industrial world has grown over time, with new methods, new procedures and new challenges, due to the availability of new technologies. This paper focus on a global survey about predictive maintenance tools that support predictive maintenance, from the time series and decision trees until Artificial Intelligence. The approach of the several tools that can help the prediction is holistic, because new tools do not eliminate the importance of the old ones: they are complimentary and each new tool that is developed add potential for a better prediction. Additionally, it must be emphasized that some tools, that seem new are, in practice, old tools with new and powerful computational devices, assuming a new and strategic importance nowadays.
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Ohoriemu, Okeoghene Blessing, and Justin Onyarin Ogala. "INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS." FUDMA JOURNAL OF SCIENCES 8, no. 3 (2024): 501–5. http://dx.doi.org/10.33003/fjs-2024-0803-2593.
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Predictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the integrated approach for predictive maintenance in an industrial setting. This article aims to provide a comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive maintenance and to provide guidance for researchers and practitioners working in this field.
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You, Ming-Yi, and Guang Meng. "A modularized framework for predictive maintenance scheduling." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 226, no. 4 (2011): 380–91. http://dx.doi.org/10.1177/1748006x11431209.
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This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.
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Pandey, Rohit. "Integration of Machine Learning Algorithms in Mechatronic Systems for Predictive Maintenance." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1822–29. http://dx.doi.org/10.17762/msea.v70i2.2475.
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The integration of machine learning algorithms in mechatronic systems has emerged as a promising approach for achieving efficient and reliable predictive maintenance strategies. This abstract provides an overview of the application of machine learning techniques in mechatronic systems for predictive maintenance, highlighting the benefits, challenges, and future directions in this field. Predictive maintenance plays a crucial role in ensuring the optimal performance and longevity of mechatronic systems, such as industrial machinery, automotive systems, and robotics. Traditional maintenance approaches rely on predetermined maintenance schedules or reactive maintenance, which can result in unnecessary downtime, high maintenance costs, and unexpected failures. To address these limitations, the integration of machine learning algorithms has gained significant attention in recent years. Machine learning algorithms offer the ability to analyse large volumes of data collected from various sensors embedded in mechatronic systems. These algorithms can identify patterns, anomalies, and trends within the data, enabling predictive maintenance decisions. By utilizing historical data, machine learning algorithms can learn the normal behaviour of the system and predict potential failures or maintenance requirements in advance.
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Shah, Chirag Vinalbhai. "Machine Learning Algorithms for Predictive Maintenance in Autonomous Vehicles." International Journal of Engineering and Computer Science 13, no. 01 (2024): 26015–32. http://dx.doi.org/10.18535/ijecs/v13i01.4786.
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The complexity and hazards of autonomous vehicle systems have posed a significant challenge in predictive maintenance. Since the incompetence of autonomous vehicle system software and hardware could lead to life-threatening crashes, maintenance should be performed regularly to protect human safety. For automotive systems, predicting future failures and taking actions in advance to maintain system reliability and safety is very crucial in large-scale product design. This paper will explore several machine learning algorithms including regression techniques, classification techniques, ensemble techniques, clustering techniques, and deep learning techniques used for system maintenance need assessment in autonomous vehicles. Experimental results indicate that predictive maintenance can be greatly helpful for autonomous vehicles either in improving system design or mitigating the risk of threats.
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Zero, Enrico, Mohamed Sallak, and Roberto Sacile. "Predictive Maintenance in IoT-Monitored Systems for Fault Prevention." Journal of Sensor and Actuator Networks 13, no. 5 (2024): 57. http://dx.doi.org/10.3390/jsan13050057.
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This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.
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Narsina, Deekshith, Krishna Devarapu, Arjun Kamisetty, Jaya Chandra Srikanth Gummadi, Nicholas Richardson, and Aditya Manikyala. "Emerging Challenges in Mechanical Systems: Leveraging Data Visualization for Predictive Maintenance." Asian Journal of Applied Science and Engineering 10, no. 1 (2021): 77–86. https://doi.org/10.18034/ajase.v10i1.124.
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This research addresses mechanical system issues and how data visualization might improve predictive maintenance (PdM). The primary goal is to examine how data visualization simplifies complicated maintenance data, improves prediction accuracy, and aids real-time decision-making. This research identifies PdM visualization trends, developments, and problems by synthesizing secondary data from academic publications, industry reports, and technical papers. Significant results show that visualization tools like real-time monitoring, AI integration, and immersive technologies like AR and VR may change PdM. These advances simplify complicated information, enable proactive maintenance, and boost mechanical system management efficiency. However, data integration, standards, and expensive implementation costs prevent wider use, especially for SMEs. The paper suggests standardization, labor training, and technology adoption incentives as governmental recommendations. Promoting PdM visualization via supporting policies would enable enterprises of all sizes to realize the full potential of predictive maintenance, boosting system dependability and operational efficiency.
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Researcher. "BEST PRACTICES FOR BUILDING AI-DRIVEN PREDICTIVE MAINTENANCE SYSTEMS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2074–85. https://doi.org/10.5281/zenodo.14332846.
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This comprehensive article explores implementing AI-driven predictive maintenance systems in industrial settings, focusing on best practices and essential components. The article examines how modern manufacturing facilities have improved operational efficiency through predictive maintenance strategies. It covers critical aspects, including data collection infrastructure, feature engineering, model selection, real-time integration, optimization, and implementation practices. The article demonstrates how organizations leveraging AI-powered predictive maintenance achieve substantial reductions in maintenance costs, improved equipment longevity, and enhanced operational efficiency. The article also highlights the importance of proper sensor deployment, data quality management, and cross-functional integration in successful implementations.
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Alok Singh. "Leveraging AI in the Manufacturing Industry for Predictive Maintenance: A Technical Perspective." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3258–67. https://doi.org/10.32628/cseit25112804.
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This technical article explores the transformative role of artificial intelligence in revolutionizing predictive maintenance practices across the manufacturing industry. By examining the integration of Internet of Things sensor networks with sophisticated machine learning algorithms, the article demonstrates how manufacturers can transition from traditional reactive maintenance approaches to proactive failure prediction systems. The discussion encompasses the technical infrastructure required for implementation, analytical models that drive accurate predictions, integration challenges with existing manufacturing systems, and methods for quantifying return on investment. Through examination of real-world applications and emerging technologies, the article provides a comprehensive framework for understanding how AI-enabled predictive maintenance solutions enhance operational efficiency, extend equipment lifespan, and deliver significant maintenance cost reductions in industrial environments.
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Farid Agayev, Stanislav Agamatov, Farid Agayev, Stanislav Agamatov. "ADVANCED MONITORING IN WATER COOLING SYSTEMS USING LSTM NETWORKS AND EXTERNAL FACTOR INTEGRATION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 145, no. 05 (2024): 44–53. https://doi.org/10.36962/pahtei149052024-44.
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This paper presents an advanced approach to monitoring and predicting the performance of water cooling systems in industrial settings. Traditional methods often relied on fixed setpoints and overlooked the impact of external factors like wind speed, treating these variations as errors. By leveraging Long Short-Term Memory (LSTM) networks, this study integrates these external influences into the predictive model, transforming what was previously seen as noise into actionable data. The result is a more accurate prediction of potential system failures and an improved understanding of how various factors affect the overall reliability of water cooling systems. Keywords: water cooling systems, LSTM networks, predictive maintenance, external factors integration, industrial monitoring.
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Khan, Armanul Islam. "Utilizing Data Analytics for Predictive Maintenance in Manufacturing: A Systematic Review on Achieving Operational Excellence." Innovatech Engineering Journal 1, no. 01 (2024): 56–67. https://doi.org/10.70937/itej.v1i01.7.
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Predictive maintenance (PdM) has emerged as a key strategy for enhancing operational efficiency in manufacturing by leveraging data analytics to forecast equipment failures and optimize maintenance activities. This paper systematically reviews the current state of predictive maintenance in manufacturing, with a particular focus on the integration of data-driven techniques, including machine learning and Internet of Things (IoT) technologies, for improved maintenance management. The review highlights the effectiveness of various predictive models, such as random forests, support vector machines, and artificial neural networks, in predicting machine failures and reducing downtime. It also explores the role of IoT sensors in real-time monitoring of equipment and the challenges associated with data quality, sensor reliability, and the integration of legacy systems. The paper examines the cost-benefit considerations of adopting predictive maintenance systems, revealing that while the initial investment can be significant, the long-term savings from reduced unplanned downtime, extended equipment lifespan, and optimized maintenance operations often justify the expenditure. Additionally, it discusses the barriers to PdM adoption, including the need for skilled labor, organizational resistance to change, and challenges related to data management. Looking ahead, the review identifies key emerging technologies, such as artificial intelligence (AI), digital twins, and edge computing, as critical enablers for the future of predictive maintenance. These technologies are expected to enhance the accuracy and real-time capabilities of predictive models, further driving efficiency in manufacturing operations. The paper concludes that while challenges remain, the continued advancement of predictive maintenance, underpinned by data analytics, will play a pivotal role in driving operational excellence and competitiveness within the manufacturing sector.
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Attia, Hussien Gomaa. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering (IJESE) 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.E2595.13050425.
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<strong>Abstract: </strong>Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoTenabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing nextgeneration digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.
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TARIK, Mouna, Ayoub MNIAI, and Khalid JEBARI. "HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES." Applied Computer Science 19, no. 2 (2023): 112–24. http://dx.doi.org/10.35784/acs-2023-18.
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The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.
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Abimbola, Olamide, Oluwafemi Tayo Ojo, Ebenezer Fagbola, Usman Abdullahi Idris, and Muhammad Bolakale Salman. "IoT-Driven Predictive Maintenance For Wind Turbines." Path of Science 11, no. 2 (2025): 6001. https://doi.org/10.22178/pos.114-20.
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Wind turbines are critical components of renewable energy infrastructure, yet their maintenance poses significant challenges due to unpredictable failures and high operational costs. This paper presents an IoT-driven predictive maintenance framework for wind turbines, leveraging advanced sensors, machine learning algorithms, and real-time data analytics. Our approach enables proactive maintenance, reduces downtime, and optimises energy production by continuously monitoring turbine performance, detecting anomalies, and predicting potential failures. We detail the system architecture, implementation, and results, demonstrating the effectiveness of the proposed framework. The study highlights the transformative potential of IoT-driven predictive maintenance in enhancing wind energy systems' reliability and efficiency while outlining future research directions to advance this field further.
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Jovančić, Predrag, Dragan Ignjatović, Stevan Đenadić, Miloš Tanasijević, and Filip Miletić. "Koncept prediktivnog održavanja 4.0 (PdM) u energetici – konekcija sa budućom primenom Industrije 5.0." Energija, ekonomija, ekologija XXIV, no. 2 (2022): 54–60. http://dx.doi.org/10.46793/eee22-2.54j.
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Industry 4.0 marks the fourth industrial revolution, characterized by the use of cyber-physical systems. In order to achieve an optimal maintenance strategy (but also production), it is necessary to develop systems that support advanced intelligent maintenance systems or smart maintenance technologies. This resulted in the postulates of Predictive Maintenance 4.0, which define the very near future in the field of maintenance of technical systems. Predictive Maintenance 4.0 involves harnessing the power of artificial intelligence to create ongoing insights into detecting causes and anomalies in equipment operations that are not detected by cognitive power. In other words, Predictive Maintenance 4.0 makes it possible to predict what was previously unpredictable. Industry 5.0 focuses on the return of human hands and minds to the industrial framework. The man and machine harmonize with each other and find ways to work together to improve production / maintenance efficiency.
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Lv, Yaqiong, Pan Zheng, Jiabei Yuan, and Xiaohua Cao. "A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction." Mathematics 11, no. 18 (2023): 3884. http://dx.doi.org/10.3390/math11183884.
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Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.
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Нікул, С., А. Дерев'янчук, О. Кравчук, Д. Максимчук та Ю. Сініло. "ВПРОВАДЖЕННЯ КОНЦЕПЦІЇ «PREDICTIVE MAINTENANCE» ДЛЯ ЗБІЛЬШЕННЯ НАДІЙНОСТІ ТА ПЕРЕДБАЧУВАНОСТІ РЕМОНТНИХ РОБІТ". Collection of scientific works of Odesa Military Academy, № 20 (14 грудня 2023): 51–55. http://dx.doi.org/10.37129/2313-7509.2023.20.51-55.
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This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the challenges that arise when implementing this concept and provides recommendations on how to overcome them. Modern military equipment requires a high level of reliability and combat readiness. The introduction of the Predictive Maintenance concept is becoming extremely important to ensure the efficiency and safety of military equipment. This research article is devoted to the study and discussion of the possibilities of implementing Predictive Maintenance in the context of rocket and artillery weapons in order to increase the reliability and predictability of repair work. In conclusion, the article emphasizes the importance of implementing Predictive Maintenance as a strategic tool to ensure optimal performance and readiness of missile and artillery weapons in the modern military environment. Keywords: Implementation, concept, Predictive Maintenance, reliability, predictability, repair work, data analytics, artificial intelligence, monitoring systems, machine learning algorithms, sensors, failure prediction, planning, efficiency, readiness, rocket and artillery weapons.
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Marjanovic, Aleksandra, Goran Kvascev, Predrag Tadic, and Zeljko Djurovic. "Applications of predictive maintenance techniques in industrial systems." Serbian Journal of Electrical Engineering 8, no. 3 (2011): 263–79. http://dx.doi.org/10.2298/sjee1103263m.
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Prognostic methods represent a new methodology for system maintenance which offers significant time and cost savings. The paper offers a short overview of the available prognosis techniques and proposes the implementation of one model-based and one data-driven method. As a representative of the model-based methods the autoregressive moving average (ARMA) modeling approach is chosen. The estimated model parameters are further used for implementing the early change detector which is realized as a Neyman-Pearson hypothesis test. On the other hand, hidden Markov model (HMM) based prognosis illustrates the use of data-driven techniques. Using the cross-correlation input-output functions, HMM prognosis algorithm is proposed, as a suitable way of timely detection. Both techniques were implemented to detect performance changes of the water level sensor in a steam separator system in thermal power plants.
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Kammerer, Christoph, Pascal Starke, Michael Gaust, Micha Küstner, Roman Radtke, and Alexander Jesser. "Comparison of Predictive Maintenance Methods for Thermal Systems." Procedia Computer Science 176 (2020): 166–74. http://dx.doi.org/10.1016/j.procs.2020.08.018.
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Efthymiou, K., N. Papakostas, D. Mourtzis, and G. Chryssolouris. "On a Predictive Maintenance Platform for Production Systems." Procedia CIRP 3 (2012): 221–26. http://dx.doi.org/10.1016/j.procir.2012.07.039.
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Nguyen, Kim-Anh, Phuc Do, and Antoine Grall. "Multi-level predictive maintenance for multi-component systems." Reliability Engineering & System Safety 144 (December 2015): 83–94. http://dx.doi.org/10.1016/j.ress.2015.07.017.
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De Benedetti, Massimiliano, Fabio Leonardi, Fabrizio Messina, Corrado Santoro, and Athanasios Vasilakos. "Anomaly detection and predictive maintenance for photovoltaic systems." Neurocomputing 310 (October 2018): 59–68. http://dx.doi.org/10.1016/j.neucom.2018.05.017.
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Lu, Susan, Yu-Chen Tu, and Huitian Lu. "Predictive condition-based maintenance for continuously deteriorating systems." Quality and Reliability Engineering International 23, no. 1 (2007): 71–81. http://dx.doi.org/10.1002/qre.823.
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Meriem, Hafsi, Hamour Nora, and Ouchani Samir. "Predictive Maintenance for Smart Industrial Systems: A Roadmap." Procedia Computer Science 220 (2023): 645–50. http://dx.doi.org/10.1016/j.procs.2023.03.082.
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Al-Humairi, Ali, Enmar Khalis, Zuhair A. Al-Hemyari, and Peter Jung. "Machine Learning-Based Predictive Maintenance for Photovoltaic Systems." AI 6, no. 7 (2025): 133. https://doi.org/10.3390/ai6070133.
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The performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system that can independently forecast and schedule cleaning sessions from real-time sensor and environmental data. Methods: The system integrates sources of data like embedded sensors, weather stations, and DustIQ data to create an integrated dataset for predictive modeling. Machine learning models were employed to forecast soiling loss based on significant atmospheric parameters such as relative humidity, air pressure, ambient temperature, and wind speed. Dimensionality reduction through the principal component analysis and correlation-based feature selection enhanced the model performance as well as the interpretability. A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. Results: Performance, based on accuracy, precision, recall, and F1-score, demonstrated that logistic regression and SVM provided optimal classification performance with accuracy levels over 92%, and F1-scores over 0.90, demonstrating outstanding balance between recall and precision. The KNN and decision tree models, while slightly poorer in terms of accuracy (around 85–88%), had computational efficiency benefits, making them suitable for utilization in resource-constrained applications. Conclusions: The proposed system employs a dry-cleaning mechanism that requires no water, making it highly suitable for arid regions. It reduces unnecessary cleaning operations by approximately 30%, leading to decreased mechanical wear and lower maintenance costs. Additionally, by minimizing delays in necessary cleaning, the system can improve annual energy yield by 3–5% under high-soiling conditions. Overall, the intelligent cleaning schedule minimizes manual intervention, enhances sustainability, reduces operating costs, and improves system performance in challenging environments.
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Kryukov, Oleg, Igor Gulyayev, and Dmitriy Teplukhov. "The Bayesian Decision Models During the Maintenance of Automated Electric Drives." Известия высших учебных заведений. Электромеханика 65, no. 3 (2022): 49–55. http://dx.doi.org/10.17213/0136-3360-2022-3-49-55.
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An analytical approach is proposed for the predictive control of technical condition of complex automated electric drive systems using forecasting, which makes it possible to increase the efficiency of maintenance and repair systems implementation based on the actual state. Based on the utilization coefficient use, a condition prediction efficiency criterion is proposed that provides reliable information on the state of AC electric drives in on-line mode. A method for preventive decision making for predicting failures of various automated electric drive systems based on the Bayesian approach has been developed. The proposed method has been tested on synchronous machines of the STD-12500-2 type, which operate as drive electric motors for gas-pumping units at linear compressor stations of the main gas transport.
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Hoffmann, Felix, Benjamin Brockhaus, Joachim Metternich, and Matthias Weigold. "Predictive Maintenance für Schutzabdeckungen/Predictive maintenance for protective covers – From business model to application." wt Werkstattstechnik online 110, no. 07-08 (2020): 496–500. http://dx.doi.org/10.37544/1436-4980-2020-07-08-40.
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Predictive Maintenance ist eines der bestimmenden Themen im Kontext von Industrie 4.0. Ein Blick in heutige Produktionsstätten zeigt jedoch, dass die Voraussetzungen zur Umsetzung dieser Technologie für den überwiegenden Teil der Industrieanwendungen bisher nicht gegeben sind. Dieser Beitrag beschäftigt sich mit den notwendigen Schritten – vom Geschäftsmodell zur Anwendung in der industriellen Praxis – am Beispiel einer Schutzabdeckung für Werkzeugmaschinen. &nbsp; Predictive maintenance is one of the defining topics in the context of Industrie 4.0, but a look at todays production facilities shows that the conditions for implementing this technology are not yet in place for the majority of industrial applications. This article is concerned with the necessary steps – from business model to application in industrial practice – using the example of a protective cover for machine tools.
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soni,, Rajat. "Enhancing Transparency and Accountability in Predictive Maintenance with Explainable AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32027.
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Predictive maintenance is a critical aspect of industrial operations, enabling proactive identification and mitigation of potential failures in machinery and equipment. However, the widespread adoption of AI-driven predictive maintenance solutions has been hindered by the opaque nature of many machines learning models, raising concerns about transparency, accountability, and trust. This research aims to address these challenges by developing explainable AI techniques for predictive maintenance in industrial systems. By integrating interpretability methods with advanced predictive models, we seek to enhance the transparency and interpretability of AI-driven maintenance decisions. Our proposed methodology combines state-of-the-art machine learning algorithms with local and global explainability techniques, such as LIME, SHAP, and feature importance analysis. Through extensive experiments on real-world industrial data, we evaluate the performance of our explainable AI models and demonstrate their ability to provide insightful explanations, enabling domain experts to understand the underlying reasoning and critical factors contributing to maintenance predictions. Furthermore, we explore the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios. Keywords— Predictive Maintenance, Explainable AI (XAI), Machine Learning, Interpretability, LIME, SHAP, Feature Importance, Industrial Systems, Trust in AI, Accountability.
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Nakhate, Vivek, Pankaj N. Patil, Rohit S. Gupta, et al. "Fuzzy Logic-Based Predictive Maintenance in Industry 4.0: Enhancing Sustainability through Uncertainty Modeling." Journal of Fuzzy Sets and Fuzzy Logic Design 2, no. 1 (2025): 39–55. https://doi.org/10.46610/jofsfld.2025.v02i01.005.
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The predictive maintenance model represents a key Industrial 4.0 operational element that both minimizes equipment failures and, slashes operational expenses and boosts operational performance. Operating with traditional PdM techniques becomes challenging because of unclear sensor data, unpredictable equipment wear, and dynamic operational settings. The research tackles uncertainty challenges in predictive maintenance through fuzzy logic-based models for uncertainty prediction. The framework of fuzzy logic functions excellently to manage uncertain and unclear data information which works best in industrial sites that face challenges determining exact failure limits. The new approach applies integrated fuzzy logic controllers with machine learning prediction systems, which improve maintenance intervention timings without generating unnecessary false signals. Our model boosts decision-making performance through linguistic variables joined with fuzzy inference systems which results in better resource management and extends equipment lifetime. The research evaluates how fuzzy logic-based PdM contributes to sustainability through prevention of superfluous maintenance work while decreasing power usage and maximizing asset usage. This study introduces three essential elements to predictive maintenance framework development under Industry 4.0: a framework based on fuzzy logic and uncertainty handling and sustainability analysis of maintenance benefits. The predicted outcomes show improvements in forecasting reliability while simultaneously reducing costs and achieving better sustainability accomplishments. The research delivers meaningful recommendations which serve industries regarding intelligent maintenance strategy deployment within operationally dynamic and uncertain conditions.
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Dosluoglu, Taner, and Martin MacDonald. "Circuit Design for Predictive Maintenance." Advances in Artificial Intelligence and Machine Learning 02, no. 04 (2022): 533–39. http://dx.doi.org/10.54364/aaiml.2022.1136.
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Industry 4.0 has become a driver for the entire manufacturing industry. Smart systems have enabled 30% productivity increases and predictive maintenance has been demonstrated to provide a 50% reduction in machine downtime. So far, the solution has been based on data analytics which has resulted in a proliferation of sensing technologies and infrastructure for data acquisition, transmission and processing. At the core of factory operation and automation are circuits that control and power factory equipment, innovative circuit design has the potential to address many system integration challenges. We present a new circuit design approach based on circuit level artificial intelligence solutions, integrated within control and calibration functional blocks during circuit design, improving the predictability and adaptability of each component for predictive maintenance. This approach is envisioned to encourage the development of new EDA tools such as automatic digital shadow generation and product lifecycle models, that will help identification of circuit parameters that adequately define the operating conditions for dynamic prediction and fault detection. Integration of a supplementary artificial intelligence block within the control loop is considered for capturing nonlinearities and gain/bandwidth constraints of the main controller and identifying changes in the operating conditions beyond the response of the controller. System integration topics are discussed regarding integration within OPC Unified Architecture and predictive maintenance interfaces,providing real-time updates to the digital shadow that help maintain an accurate, virtual replica model of the physical system.
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Sarje, S. H. "Integration of maintenance systems." MATEC Web of Conferences 211 (2018): 03010. http://dx.doi.org/10.1051/matecconf/201821103010.
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Excellence in maintenance is imperative in highly competitive market because it resulted into minimum maintenance cost, high equipment effectiveness, maximum reliability of the system, high quality of the products, low delivery time, high flexibility, safety etc. Any maintenance system such as Total Productive Maintenance (TPM) or Reliability Centered Maintenance (RCM) or Condition Based Maintenance (CBM) alone cannot achieve the excellence in maintenance but its integration may do. In this paper, an integration of TPM, RCM and CBM is proposed with a maintenance policy to take advantage of their respective strengths. A continuously monitored system subject to degradation due to the imperfect maintenance, where a hybrid hazard rate based on the concept of age reduction factor and hazard rate increase factor to predict the evolution of the system reliability in different maintenance cycles has been assumed.A quantitative decision making model for an integrated maintenance system is derived in order to assess the performance of the proposed maintenance policy. Numerical examples of calculation of optimal preventive maintenance age x and preventive maintenance number N* for the given cost ratio of corrective replacement and predictive preventive maintenance are given.