Integrating Machine Learning-Based Remaining Useful Life Predictions with Cost-Optimal Block Replacement for Industrial Maintenance
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Published
Apr 21, 2025
YOUNGSUK CHOO
Seung-Jun Shin
Abstract
This study presents a preventive maintenance methodology to predict the remaining useful life (RUL) of mechanical systems and determine cost-effective replacement schedules. The approach integrates machine learning for RUL prediction, Weibull distribution for reliability analysis, and a block replacement model with minimal repair to optimize preventive maintenance. Many existing studies rarely incorporate RUL prediction results into determining optimal maintenance actions due to the high uncertainty in RUL prediction. To address this, the proposed methodology emphasizes not stopping at the prediction stage but integrating RUL predictions into actionable maintenance strategies to reduce uncertainty and improve applicability in industrial contexts. A case study using the open CMAPSS dataset demonstrates the feasibility of the approach. The value of this study lies in proposing a methodology that not only utilizes prediction-based proactive outcomes instead of predefined replacement intervals but also integrates them with subsequent maintenance strategies, providing practical and cost-effective solutions for industrial applications.
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Keywords
Remaining useful life, Predictive maintenance, Preventive maintenance, Block replacement model, Machine learning
References
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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
Cailian, L., & Chun, Z. (2020). Life prediction of battery based on random forest optimized by genetic algorithm. 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 19950220. https://doi.org/10.1109/ICPHM49022.2020.9187060
Chao, M.A., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft Engine Run-to-Failure Dataset Under Real Flight Conditions for Prognostics and Diagnostics. Data, 6(1), 5. https://doi.org/10.3390/data6010005
Chen, C., Shi, J., Lu, N., Zhu, Z.H. & Jiang, B. (2022). Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction. Neurocomputing, 494, 79-88. https://doi.org/10.1016/j.neucom.2022.04.055
Dong, W., Liu, s., Cao, Y., & Bae S. (2020). Time-based replacement policies for a fault tolerant system subject to degradation and two types of shocks. Quality and Reliability Engineering International, 36(7), 2338–2350. https://doi.org/10.1002/qre.2700
Ferreira, C., & Gonçalves, G. (2022). Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems, 63, 550–562. https://doi.org/10.1016/j.jmsy.2022.05.010
Frederick, D., DeCastro, J., & Litt, J. (2007). User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). NASA/TM-2007-215026.
Jin, L., & Yamamoto, W. (2017). Adaptive age replacement using on-line monitoring. Procedia Engineering, 174, 117–125. https://doi.org/10.1016/j.proeng.2017.01.177
Kraus M., & Feuerriegel, S. (2019). Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Decision Support Systems, Vol. 125, 113100. https://doi.org/10.1016/j.dss.2019.113100
Lei, Y., Li, N., Guo, L., Li N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
Liu, L., Wang, L., & Yu, Z. (2021). Remaining useful life estimation of aircraft engines based on deep convolution neural network and lightGBM combination model. International Journal of Computational Intelligence Systems, 14, 165. https://doi.org/10.1007/s44196-021-00020-1
Lv., Y., Guo, X., Zhou, Q., Qian, L. & Liu, J. (2023). Predictive maintenance decision-making for variable faults with non-equivalent costs of fault severities. Advanced Engineering Informatics, 56. https://doi.org/10.1016/j.aei.2023.102011
Najdi, B., Benbrahim, M.& Kabbaj, M.N. (2024). Adaptive Res-LSTM attention-based remaining useful lifetime prognosis of rolling bearings. International Journal of Prognostics and Health Management, https://doi.org/10.36001/ijphm.2025.v16i1.4171
Nakagawa, T. (1979). A summary of block replacement policies. Operations Research, 4, 351–361.
Narayanan, L.K., Loganayagi, S., Hemavathi, R., Jayalakshmi, D. & Vimal V.R. (2024). Machine Learning-Based Predictive Maintenance for Industrial Equipment Optimization. 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies. https://doi.org/ 10.1109/TQCEBT59414.2024.10545280
Park, P., Jung, M., & Di Marco, P. (2020). Remaining useful life estimation of bearings using data-driven ridge regression. Applied Sciences, 10(24), 8977. https://doi.org/10.3390/app10248977
Rausand, M., & Hoyland, A. (2004). System reliability theory models, statistical methods, and applications. Press, Wiley.
Rebaiaia, M.L., Ait-Kadi, D., & Jamshidi, A. (2017). Periodic replacement strategies: optimality conditions and numerical performance comparisons. International Journal of Production Research, 55(23), 7135–7152. https://doi.org/10.1080/00207543.2017.1349953
Rebaiaia, ML., & Ait-kadi, D. (2020). Maintenance policies with minimal repair and replacement on failures: analysis and comparison. International Journal of Production Research, 59(23), 6995–7017. https://doi.org/10.1080/00207543.2020.1832275
Ross, S.M. (1980). Introduction to probability models. Academic, Press, N.Y.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 International Conference on Prognostics and Health Management, 10423504. https://doi.org/10.1109/PHM.2008.4711414
Shi, J., Yu, T., Goebel, K., & Wu, D. (2021). Remaining useful life prediction of bearings using ensemble learning: The impact of diversity in base learners and features. Journal of Computing and Information Science in Engineering, 21(2), 021004. https://doi.org/10.1115/1.4048215
Thyago P. Carvalho, Fabrízzio A. A. M. N. Soares, Roberto Vita, Roberto da P. Francisco, João P. Basto, & Symone G. S. Alcalá. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Tong, Z., Miao, J., Tong, S., & Lu, Y. (2021). Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method. Journal of Cleaner Production, 317, 128265. https://doi.org/10.1016/j.jclepro.2021.128265
Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139, 469–489. https://doi.org/10.1016/S0377-2217(01)00197-7
Woo, J., Shin, S., Seo, W., & Meilanitasari, M. (2018). Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. The International Journal of Advanced Manufacturing Technology, 99, 2193–2217. https://doi.org/10.1007/s00170-018-2416-9
Wu, J.Y., Wu, M., Chen, Z., Li, X., & Yan, R. (2021). A joint classification-regression method for multi-stage remaining useful life prediction. Journal of Manufacturing Systems, 58, 109–119. https://doi.org/10.1016/j.jmsy.2020.11.016
Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020). Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing, 376, 95–102. https://doi.org/10.1016/j.neucom.2019.09.074
Zhao, X., A-Kalifa, KN., Hamouda, AM., & Nakagawa, T. (2017). Age replacement models: A summary with new perspectives and methods. Reliability Engineering and System Safety, 161, 95–105. https://doi.org/10.1016/j.ress.2017.01.011
Zonta, T., Costa, C., Zeiser, F.A., Ramos, G., Kunst, R. & Righi, R. (2022). A predictive maintenance model for optimizing production schedule using deep neural networks. Journal of Manufacturing Systems, 62. 450-462. https://doi.org/10.1016/j.jmsy.2021.12.013
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
Cailian, L., & Chun, Z. (2020). Life prediction of battery based on random forest optimized by genetic algorithm. 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 19950220. https://doi.org/10.1109/ICPHM49022.2020.9187060
Chao, M.A., Kulkarni, C., Goebel, K., & Fink, O. (2021). Aircraft Engine Run-to-Failure Dataset Under Real Flight Conditions for Prognostics and Diagnostics. Data, 6(1), 5. https://doi.org/10.3390/data6010005
Chen, C., Shi, J., Lu, N., Zhu, Z.H. & Jiang, B. (2022). Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction. Neurocomputing, 494, 79-88. https://doi.org/10.1016/j.neucom.2022.04.055
Dong, W., Liu, s., Cao, Y., & Bae S. (2020). Time-based replacement policies for a fault tolerant system subject to degradation and two types of shocks. Quality and Reliability Engineering International, 36(7), 2338–2350. https://doi.org/10.1002/qre.2700
Ferreira, C., & Gonçalves, G. (2022). Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems, 63, 550–562. https://doi.org/10.1016/j.jmsy.2022.05.010
Frederick, D., DeCastro, J., & Litt, J. (2007). User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). NASA/TM-2007-215026.
Jin, L., & Yamamoto, W. (2017). Adaptive age replacement using on-line monitoring. Procedia Engineering, 174, 117–125. https://doi.org/10.1016/j.proeng.2017.01.177
Kraus M., & Feuerriegel, S. (2019). Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Decision Support Systems, Vol. 125, 113100. https://doi.org/10.1016/j.dss.2019.113100
Lei, Y., Li, N., Guo, L., Li N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
Liu, L., Wang, L., & Yu, Z. (2021). Remaining useful life estimation of aircraft engines based on deep convolution neural network and lightGBM combination model. International Journal of Computational Intelligence Systems, 14, 165. https://doi.org/10.1007/s44196-021-00020-1
Lv., Y., Guo, X., Zhou, Q., Qian, L. & Liu, J. (2023). Predictive maintenance decision-making for variable faults with non-equivalent costs of fault severities. Advanced Engineering Informatics, 56. https://doi.org/10.1016/j.aei.2023.102011
Najdi, B., Benbrahim, M.& Kabbaj, M.N. (2024). Adaptive Res-LSTM attention-based remaining useful lifetime prognosis of rolling bearings. International Journal of Prognostics and Health Management, https://doi.org/10.36001/ijphm.2025.v16i1.4171
Nakagawa, T. (1979). A summary of block replacement policies. Operations Research, 4, 351–361.
Narayanan, L.K., Loganayagi, S., Hemavathi, R., Jayalakshmi, D. & Vimal V.R. (2024). Machine Learning-Based Predictive Maintenance for Industrial Equipment Optimization. 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies. https://doi.org/ 10.1109/TQCEBT59414.2024.10545280
Park, P., Jung, M., & Di Marco, P. (2020). Remaining useful life estimation of bearings using data-driven ridge regression. Applied Sciences, 10(24), 8977. https://doi.org/10.3390/app10248977
Rausand, M., & Hoyland, A. (2004). System reliability theory models, statistical methods, and applications. Press, Wiley.
Rebaiaia, M.L., Ait-Kadi, D., & Jamshidi, A. (2017). Periodic replacement strategies: optimality conditions and numerical performance comparisons. International Journal of Production Research, 55(23), 7135–7152. https://doi.org/10.1080/00207543.2017.1349953
Rebaiaia, ML., & Ait-kadi, D. (2020). Maintenance policies with minimal repair and replacement on failures: analysis and comparison. International Journal of Production Research, 59(23), 6995–7017. https://doi.org/10.1080/00207543.2020.1832275
Ross, S.M. (1980). Introduction to probability models. Academic, Press, N.Y.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 International Conference on Prognostics and Health Management, 10423504. https://doi.org/10.1109/PHM.2008.4711414
Shi, J., Yu, T., Goebel, K., & Wu, D. (2021). Remaining useful life prediction of bearings using ensemble learning: The impact of diversity in base learners and features. Journal of Computing and Information Science in Engineering, 21(2), 021004. https://doi.org/10.1115/1.4048215
Thyago P. Carvalho, Fabrízzio A. A. M. N. Soares, Roberto Vita, Roberto da P. Francisco, João P. Basto, & Symone G. S. Alcalá. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Tong, Z., Miao, J., Tong, S., & Lu, Y. (2021). Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method. Journal of Cleaner Production, 317, 128265. https://doi.org/10.1016/j.jclepro.2021.128265
Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139, 469–489. https://doi.org/10.1016/S0377-2217(01)00197-7
Woo, J., Shin, S., Seo, W., & Meilanitasari, M. (2018). Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. The International Journal of Advanced Manufacturing Technology, 99, 2193–2217. https://doi.org/10.1007/s00170-018-2416-9
Wu, J.Y., Wu, M., Chen, Z., Li, X., & Yan, R. (2021). A joint classification-regression method for multi-stage remaining useful life prediction. Journal of Manufacturing Systems, 58, 109–119. https://doi.org/10.1016/j.jmsy.2020.11.016
Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020). Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing, 376, 95–102. https://doi.org/10.1016/j.neucom.2019.09.074
Zhao, X., A-Kalifa, KN., Hamouda, AM., & Nakagawa, T. (2017). Age replacement models: A summary with new perspectives and methods. Reliability Engineering and System Safety, 161, 95–105. https://doi.org/10.1016/j.ress.2017.01.011
Zonta, T., Costa, C., Zeiser, F.A., Ramos, G., Kunst, R. & Righi, R. (2022). A predictive maintenance model for optimizing production schedule using deep neural networks. Journal of Manufacturing Systems, 62. 450-462. https://doi.org/10.1016/j.jmsy.2021.12.013
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Technical Papers