From Corrective and Preventive Maintenance to Prognostics The Evolution of Machinery Maintenance and Health Management for Enhanced System Reliability and Efficiency
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Published
Apr 5, 2026
Stamatis Apeiranthitis
Christos Drosos
Avraam Chatzopoulos
Michail Papoutsidakis
Evangelos Pallis
Abstract
The modern industry faces the challenge of prolonging the lifespan of high-performance systems while maintaining their operational efficiency and cost-effectiveness. Through well-planned maintenance strategies, industries can achieve significant reductions in life cycle costs while minimising operational downtime and improving system reliability and its availability. Prognostics and Health Management (PHM) is an integrative, system-level engineering framework that combines diagnostics, prognostics, and decision-support processes to enable informed asset health management throughout the system lifecycle. While diagnostics and prognostics are core components of condition-based maintenance (CBM) and predictive maintenance (PdM), PHM emphasises their systematic integration within a closed-loop process that links operational activities with organizational and life cycle considerations. Thus, PHM does not replace existing maintenance policies; rather, it provides a unified framework that aligns health information, prognostic outputs, and maintenance decisions with resource constraints, risk, and performance objectives. Despite rapid advances in algorithmic research, a significant gap exists in the foundational conceptual understanding required by practitioners and researchers who are new to the field.
During a literature review of machine and deep learning applications in the field of Predictive Maintenance (PdM) and estimation of the Remaining Useful Life (RUL), the authors observed that while extensive attention is given to algorithmic developments and application-specific studies, a consolidated perspective on PHM's fundamental role, historical evolution, and strategic implementation of PHM remains notably absent. This perspective article addresses this gap by providing a clear conceptual framework and practical roadmap of maintenance models, ranging from corrective and preventive to condition-based and predictive approaches, rather than introducing new models. This study articulates PHM’s core functional elements of PHM and positions it as an integrative, system-level paradigm that contextualises established maintenance strategies, such as CBM and PdM. In addition, it discusses the technological and industrial drivers that led to the emergence of PHM, providing an accessible entry point for researchers and practitioners.
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Keywords
Predictive Maintenance, Prognostics and Health Management, Remaining Useful life, Prognostics, Diagnostics, Maintenance, Artificial Intelligence, Uncertainty, Industry 5.0
References
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Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2020). Prognostics and Health Management for Maintenance Practitioners—Review, Implementation and Tools Evaluation. International Journal of Prognostics and Health Management, 8(3). https://doi.org/10.36001/ijphm.2017.v8i3.2667
Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3, 578613. https://doi.org/10.3389/frai.2020.578613
Chokr, B., Chatti, N., Charki, A., Lemenand, T., & Hammoud, M. (2024). Bi-LSTM Autoencoder SCADA based Unsupervised Anomaly Detection in Real Wind Farm Data. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 174–183. https://doi.org/10.1109/ICPHM61352.2024.10626815
Dewey, H. H., DeVries, D. R., & Hyde, S. R. (2019). Uncertainty Quantification in Prognostic Health Management Systems. 2019 IEEE Aerospace Conference, 1–13. https://doi.org/10.1109/AERO.2019.8741821
Ding, Z.-Q., Qin, Q., Zhang, Y.-F., & Lin, Y.-H. (2024). An Uncertainty Quantification and Calibration Framework for RUL Prediction and Accuracy Improvement. IEEE Transactions on Instrumentation and Measurement, 73, 1–13. https://doi.org/10.1109/TIM.2024.3485392
Douglas S Thomas & Brian Weiss. (2021). Maintenance Costs and Advanced Maintenance Techniques: Survey and Analysis. International Journal of Prognostics and Health Management, 12(1). https://doi.org/10.36001/ijphm.2021.v12i1.2883
Enrico Zio. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/j.ress.2021.108119
Enshaei, N., Chen, H., Naderkhani, F., Lin, J. J., Shahsafi, S., Giliyana, S., Mirzaei, M., Li, S., Hansen, C., & Rupe, J. W. (2024). ICPHM’23 Benchmark Vibration Dataset Applicable in Machine Learning for Systems’ Health Monitoring. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–8. https://doi.org/10.1109/ICPHM61352.2024.10626846
Fang, Z., Li, W., Su, L., & Feng, J. (2024). Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review. Applied Sciences, 14(14), Article 14. https://doi.org/10.3390/app14146120
Flavio Trojan. (2016). Sorting maintenance types by multi-criteria analysis to clarify maintenance concepts in POM. Research Gate. https://doi.org/researchgate.net/publication/301549738
Guillén, A. J., Crespo, A., Macchi, M., & Gómez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning & Control, 27(12), 991–1004. https://doi.org/10.1080/09537287.2016.1171920
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, 217, 108063. https://doi.org/10.1016/j.ress.2021.108063
Huang, C., Bu, S., Lee, H. H., Chan, C. H., Kong, S. W., & Yung, W. K. C. (2024). Prognostics and health management for predictive maintenance: A review. Journal of Manufacturing Systems, 75, 78–101. https://doi.org/10.1016/j.jmsy.2024.05.021
IEEE Xplore. (2025). International Conference on Prognostics and Health Management, PHM - Conference Table of Contents | IEEE Xplore. https://ieeexplore.ieee.org/xpl/conhome/1002538/all-proceedings
J. Yang, D. Sun, L. Wang, W. Zhang, & X. Wang. (2025). DPMA: Self-Supervised Dual-Path Meta Alignment Network for Remaining Useful Life Prediction with Limited Data and Unknown Working Conditions. IEEE Transactions on Instrumentation and Measurement, 1–1. https://doi.org/10.1109/TIM.2025.3595222
J. Zhou & Y. Qin. (2025). A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint. IEEE Transactions on Neural Networks and Learning Systems, 36(7), 11847–11860. https://doi.org/10.1109/TNNLS.2024.3462723
Jasiulewicz-Kaczmarek, M., Legutko, S., & Kluk, P. (2020). Maintenance 4.0 technologies – new opportunities for sustainability driven maintenance. Management and Production Engineering Review. https://doi.org/10.24425/mper.2020.133730
Kalafatelis, A. S., Nomikos, N., Giannopoulos, A., Alexandridis, G., Karditsa, A., & Trakadas, P. (2025). Towards Predictive Maintenance in the Maritime Industry: A Component-Based Overview. Journal of Marine Science and Engineering, 13(3), 425. https://doi.org/10.3390/jmse13030425
Kalgren, P., Byington, C., Roemer, M., & Watson, M. (2006). Defining PHM, A Lexical Evolution of Maintenance and Logistics. 2006 IEEE Autotestcon, 353–358. https://doi.org/10.1109/AUTEST.2006.283685
Khanna, P., Kumari, J., & Karim, R. (2024). Human-Centric PHM in the Era of Industry 5.0. PHM Society European Conference, 8(1), 7. https://doi.org/10.36001/phme.2024.v8i1.4121
Kim, N.-H., An, D., & Choi, J.-H. (2017). Prognostics and Health Management of Engineering Systems. Springer International Publishing. https://doi.org/10.1007/978-3-319-44742-1
Kreuzer, M., Schmidt, D., Wokusch, S., & Kellermann, W. (2024). Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 138–145. https://doi.org/10.1109/ICPHM61352.2024.10626489
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent Maintenance Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805. https://doi.org/10.1115/1.4047856
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
Li, R., Verhagen, W. J. C., & Curran, R. (2018). A Functional Architecture of Prognostics and Health Management using a Systems Engineering Approach.
Li, X., Teng, W., Wang, L., Hu, J., Su, Y., Peng, D., & Liu, Y. (2025). Trend-constrained pairing based incremental transfer learning for remaining useful life prediction of bearings in wind turbines. Expert Systems with Applications, 263. Scopus. https://doi.org/10.1016/j.eswa.2024.125731
Liu, X., Liu, J., Sun, B., & Zhang, W. (2024). An Integrated Framework of Fourier Transform and Transformer for Rotating Machinery Fault Diagnosis. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 161–166. https://doi.org/10.1109/ICPHM61352.2024.10627117
Mao, R., Li, Y., Li, G., Petter Hildre, H., & Zhang, H. (2025). A Systematic Survey of Digital Twin Applications: Transferring Knowledge From Automotive and Aviation to Maritime Industry. IEEE Transactions on Intelligent Transportation Systems, 26(4), 4240–4259. https://doi.org/10.1109/TITS.2025.3535593
Martín Silva, G. S., & López Droguett, E. (2024). Quantum Kernel Functions for the Prognosis and Health Management of Ball-Bearing Elements. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 257–264. https://doi.org/10.1109/ICPHM61352.2024.10626686
Mishra, M. (2018). Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation. Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Division of Operation and Maintenance Engineering.
Oh, H. J., Yoo, J., Kim, T. H., Kim, M., & Kim, H. (2024). MALSTM-MCN Ensemble Learning-based Planetary Gearbox Fault Diagnosis method. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 9–14. https://doi.org/10.1109/ICPHM61352.2024.10626919
PHM Society. (2025). International Journal of Prognostics and Health Management. https://phmsociety.org/wp-activate.php
Poór, P., Ženíšek, D., & Basl, J. (2019). Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial Revolutions.
Q. Lu, M. Li, & X. Huang. (2025). Digital Twin and Data-Driven Remaining Useful Life Prediction of Gearbox. IEEE Access, 13, 111614–111627. https://doi.org/10.1109/ACCESS.2025.3583313
Ramadhan, B. R., & Cahit, P. (2025). A deep residual sequence autoencoder for future state estimation and aiding prognostics and diagnostics in machines: A case study of mechanical rolling elements. Neural Computing and Applications, 37(17), 10737–10756. https://doi.org/10.1007/s00521-024-10756-4
Ramezani, S., Moini, A., & Riahi, M. (2019). Prognostics and Health Management in Machinery: A Review of Methodologies for RUL prediction and Roadmap. International Journal of Industrial Engineering, 6(2).
Raouf, I., Kumar, P., Khalid, S., & Kim, H. S. (2025). Comprehensive Analysis of Current Developments, Challenges, and Opportunities for the Health Assessment of Smart Factory. International Journal of Precision Engineering and Manufacturing-Green Technology, 12(4), 1321–1338. https://doi.org/10.1007/s40684-025-00694-4
Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement: Journal of the International Measurement Confederation, 163. https://doi.org/10.1016/j.measurement.2020.107929
Sayyad, S., Kumar, S., Bongale, A., Kotecha, K., & Abraham, A. (2023). Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models. Sensors, 23(12), 5659. https://doi.org/10.3390/s23125659
Semeraro, C., Jarrar, K. F., Saleh, M. B., Alhammadi, A. K., Ababneh, R. F. H., & Alami, A. H. (2024). Enhance Efficiency: Integrating Prognostic and Health Management with Business Intelligence. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 205–211. https://doi.org/10.1109/ICPHM61352.2024.10627207
Sheppard, J. W., Kaufman, M. A., & Wilmering, T. J. (2008). IEEE Standards for Prognostics and Health Management.
Shimizu, M., Perinpanayagam, S., Namoano, B., & Starr, A. (2023). Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets. IEEE Access, 11, 28724–28734. https://doi.org/10.1109/ACCESS.2023.3259221
Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, 103335. https://doi.org/10.1016/j.compind.2020.103335
Sim, J., Min, J., Kim, D., Cho, S. H., Kim, S., & Choi, J.-H. (2022). A python based tutorial on prognostics and health management using vibration signal: Signal processing, feature extraction and feature selection. Journal of Mechanical Science and Technology, 36(8), 4083–4097. https://doi.org/10.1007/s12206-022-0728-z
Torre, N., Leo, C., & Bonamigo, A. (2023). Lean 4.0: An analytical approach for hydraulic system maintenance in a production line of steel making plant. International Journal of Industrial Engineering and Management, 14(3), 186–199. https://doi.org/10.24867/IJIEM-2023-3-332
Wyatts. (2005). The Bathtub curve. https://commons.wikimedia.org/wiki/File:Bathtub_curve.jpg
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, 178, 255–268. https://doi.org/10.1016/j.ress.2018.06.021
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Ahmed Murtaza, A., Saher, A., Hamza Zafar, M., Kumayl Raza Moosavi, S., Faisal Aftab, M., & Sanfilippo, F. (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges, and case study. Results in Engineering, 24, 102935. https://doi.org/10.1016/j.rineng.2024.102935
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2020). Prognostics and Health Management for Maintenance Practitioners—Review, Implementation and Tools Evaluation. International Journal of Prognostics and Health Management, 8(3). https://doi.org/10.36001/ijphm.2017.v8i3.2667
Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3, 578613. https://doi.org/10.3389/frai.2020.578613
Chokr, B., Chatti, N., Charki, A., Lemenand, T., & Hammoud, M. (2024). Bi-LSTM Autoencoder SCADA based Unsupervised Anomaly Detection in Real Wind Farm Data. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 174–183. https://doi.org/10.1109/ICPHM61352.2024.10626815
Dewey, H. H., DeVries, D. R., & Hyde, S. R. (2019). Uncertainty Quantification in Prognostic Health Management Systems. 2019 IEEE Aerospace Conference, 1–13. https://doi.org/10.1109/AERO.2019.8741821
Ding, Z.-Q., Qin, Q., Zhang, Y.-F., & Lin, Y.-H. (2024). An Uncertainty Quantification and Calibration Framework for RUL Prediction and Accuracy Improvement. IEEE Transactions on Instrumentation and Measurement, 73, 1–13. https://doi.org/10.1109/TIM.2024.3485392
Douglas S Thomas & Brian Weiss. (2021). Maintenance Costs and Advanced Maintenance Techniques: Survey and Analysis. International Journal of Prognostics and Health Management, 12(1). https://doi.org/10.36001/ijphm.2021.v12i1.2883
Enrico Zio. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/j.ress.2021.108119
Enshaei, N., Chen, H., Naderkhani, F., Lin, J. J., Shahsafi, S., Giliyana, S., Mirzaei, M., Li, S., Hansen, C., & Rupe, J. W. (2024). ICPHM’23 Benchmark Vibration Dataset Applicable in Machine Learning for Systems’ Health Monitoring. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–8. https://doi.org/10.1109/ICPHM61352.2024.10626846
Fang, Z., Li, W., Su, L., & Feng, J. (2024). Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review. Applied Sciences, 14(14), Article 14. https://doi.org/10.3390/app14146120
Flavio Trojan. (2016). Sorting maintenance types by multi-criteria analysis to clarify maintenance concepts in POM. Research Gate. https://doi.org/researchgate.net/publication/301549738
Guillén, A. J., Crespo, A., Macchi, M., & Gómez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning & Control, 27(12), 991–1004. https://doi.org/10.1080/09537287.2016.1171920
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, 217, 108063. https://doi.org/10.1016/j.ress.2021.108063
Huang, C., Bu, S., Lee, H. H., Chan, C. H., Kong, S. W., & Yung, W. K. C. (2024). Prognostics and health management for predictive maintenance: A review. Journal of Manufacturing Systems, 75, 78–101. https://doi.org/10.1016/j.jmsy.2024.05.021
IEEE Xplore. (2025). International Conference on Prognostics and Health Management, PHM - Conference Table of Contents | IEEE Xplore. https://ieeexplore.ieee.org/xpl/conhome/1002538/all-proceedings
J. Yang, D. Sun, L. Wang, W. Zhang, & X. Wang. (2025). DPMA: Self-Supervised Dual-Path Meta Alignment Network for Remaining Useful Life Prediction with Limited Data and Unknown Working Conditions. IEEE Transactions on Instrumentation and Measurement, 1–1. https://doi.org/10.1109/TIM.2025.3595222
J. Zhou & Y. Qin. (2025). A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint. IEEE Transactions on Neural Networks and Learning Systems, 36(7), 11847–11860. https://doi.org/10.1109/TNNLS.2024.3462723
Jasiulewicz-Kaczmarek, M., Legutko, S., & Kluk, P. (2020). Maintenance 4.0 technologies – new opportunities for sustainability driven maintenance. Management and Production Engineering Review. https://doi.org/10.24425/mper.2020.133730
Kalafatelis, A. S., Nomikos, N., Giannopoulos, A., Alexandridis, G., Karditsa, A., & Trakadas, P. (2025). Towards Predictive Maintenance in the Maritime Industry: A Component-Based Overview. Journal of Marine Science and Engineering, 13(3), 425. https://doi.org/10.3390/jmse13030425
Kalgren, P., Byington, C., Roemer, M., & Watson, M. (2006). Defining PHM, A Lexical Evolution of Maintenance and Logistics. 2006 IEEE Autotestcon, 353–358. https://doi.org/10.1109/AUTEST.2006.283685
Khanna, P., Kumari, J., & Karim, R. (2024). Human-Centric PHM in the Era of Industry 5.0. PHM Society European Conference, 8(1), 7. https://doi.org/10.36001/phme.2024.v8i1.4121
Kim, N.-H., An, D., & Choi, J.-H. (2017). Prognostics and Health Management of Engineering Systems. Springer International Publishing. https://doi.org/10.1007/978-3-319-44742-1
Kreuzer, M., Schmidt, D., Wokusch, S., & Kellermann, W. (2024). Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 138–145. https://doi.org/10.1109/ICPHM61352.2024.10626489
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent Maintenance Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805. https://doi.org/10.1115/1.4047856
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
Li, R., Verhagen, W. J. C., & Curran, R. (2018). A Functional Architecture of Prognostics and Health Management using a Systems Engineering Approach.
Li, X., Teng, W., Wang, L., Hu, J., Su, Y., Peng, D., & Liu, Y. (2025). Trend-constrained pairing based incremental transfer learning for remaining useful life prediction of bearings in wind turbines. Expert Systems with Applications, 263. Scopus. https://doi.org/10.1016/j.eswa.2024.125731
Liu, X., Liu, J., Sun, B., & Zhang, W. (2024). An Integrated Framework of Fourier Transform and Transformer for Rotating Machinery Fault Diagnosis. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 161–166. https://doi.org/10.1109/ICPHM61352.2024.10627117
Mao, R., Li, Y., Li, G., Petter Hildre, H., & Zhang, H. (2025). A Systematic Survey of Digital Twin Applications: Transferring Knowledge From Automotive and Aviation to Maritime Industry. IEEE Transactions on Intelligent Transportation Systems, 26(4), 4240–4259. https://doi.org/10.1109/TITS.2025.3535593
Martín Silva, G. S., & López Droguett, E. (2024). Quantum Kernel Functions for the Prognosis and Health Management of Ball-Bearing Elements. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 257–264. https://doi.org/10.1109/ICPHM61352.2024.10626686
Mishra, M. (2018). Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation. Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Division of Operation and Maintenance Engineering.
Oh, H. J., Yoo, J., Kim, T. H., Kim, M., & Kim, H. (2024). MALSTM-MCN Ensemble Learning-based Planetary Gearbox Fault Diagnosis method. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 9–14. https://doi.org/10.1109/ICPHM61352.2024.10626919
PHM Society. (2025). International Journal of Prognostics and Health Management. https://phmsociety.org/wp-activate.php
Poór, P., Ženíšek, D., & Basl, J. (2019). Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial Revolutions.
Q. Lu, M. Li, & X. Huang. (2025). Digital Twin and Data-Driven Remaining Useful Life Prediction of Gearbox. IEEE Access, 13, 111614–111627. https://doi.org/10.1109/ACCESS.2025.3583313
Ramadhan, B. R., & Cahit, P. (2025). A deep residual sequence autoencoder for future state estimation and aiding prognostics and diagnostics in machines: A case study of mechanical rolling elements. Neural Computing and Applications, 37(17), 10737–10756. https://doi.org/10.1007/s00521-024-10756-4
Ramezani, S., Moini, A., & Riahi, M. (2019). Prognostics and Health Management in Machinery: A Review of Methodologies for RUL prediction and Roadmap. International Journal of Industrial Engineering, 6(2).
Raouf, I., Kumar, P., Khalid, S., & Kim, H. S. (2025). Comprehensive Analysis of Current Developments, Challenges, and Opportunities for the Health Assessment of Smart Factory. International Journal of Precision Engineering and Manufacturing-Green Technology, 12(4), 1321–1338. https://doi.org/10.1007/s40684-025-00694-4
Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement: Journal of the International Measurement Confederation, 163. https://doi.org/10.1016/j.measurement.2020.107929
Sayyad, S., Kumar, S., Bongale, A., Kotecha, K., & Abraham, A. (2023). Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models. Sensors, 23(12), 5659. https://doi.org/10.3390/s23125659
Semeraro, C., Jarrar, K. F., Saleh, M. B., Alhammadi, A. K., Ababneh, R. F. H., & Alami, A. H. (2024). Enhance Efficiency: Integrating Prognostic and Health Management with Business Intelligence. 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 205–211. https://doi.org/10.1109/ICPHM61352.2024.10627207
Sheppard, J. W., Kaufman, M. A., & Wilmering, T. J. (2008). IEEE Standards for Prognostics and Health Management.
Shimizu, M., Perinpanayagam, S., Namoano, B., & Starr, A. (2023). Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets. IEEE Access, 11, 28724–28734. https://doi.org/10.1109/ACCESS.2023.3259221
Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, 103335. https://doi.org/10.1016/j.compind.2020.103335
Sim, J., Min, J., Kim, D., Cho, S. H., Kim, S., & Choi, J.-H. (2022). A python based tutorial on prognostics and health management using vibration signal: Signal processing, feature extraction and feature selection. Journal of Mechanical Science and Technology, 36(8), 4083–4097. https://doi.org/10.1007/s12206-022-0728-z
Torre, N., Leo, C., & Bonamigo, A. (2023). Lean 4.0: An analytical approach for hydraulic system maintenance in a production line of steel making plant. International Journal of Industrial Engineering and Management, 14(3), 186–199. https://doi.org/10.24867/IJIEM-2023-3-332
Wyatts. (2005). The Bathtub curve. https://commons.wikimedia.org/wiki/File:Bathtub_curve.jpg
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