Joint Prescriptive Maintenance and Production Planning and Control Process Simulation for Extrusion System
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Published
Jun 26, 2024
Kevin Wesendrup
Bernd Hellingrath
Abstract
Production planning and control (PPC) is the mainstay of every manufacturer and ensures flawless production processes. However, PPC is jeopardized by breakdowns that can only be tackled with appropriate maintenance. In the past, static strategies, such as reactive and scheduled maintenance, have been used. Yet, with growing system complexity, Industry 4.0, and abundant sensor data, dynamic strategies through PHM have emerged. The most advanced maintenance strategy is prescriptive maintenance (PxM), which allows manufacturers not only to predict failures but also to establish condition-based production plans and controls. To this end, our study explores the integration of PxM with PPC. First, we propose a fault prediction model based on health indicators and future loads of a single-machine system. The proposed fault prediction is integrated into a joint PxM and PPC simulation model that compares the makespan of three joint PxM and PPC strategies inter se and versus reactive and scheduled maintenance. A simulation study using industrial data from an extrusion process evaluates the different strategies across different time horizons (one month to a year). The findings indicate that joint PxM and PPC outperform other strategies, providing significant time savings over traditional methods. Further, a sensitivity analysis is conducted to assess the robustness of the PxM strategies under varying levels of measurement noise, revealing potential challenges under high noise conditions. The study contributes to the field of PHM by providing insights into the effectiveness of joint PxM and PPC strategies and offering a comprehensive analysis of their performance under different conditions.
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Keywords
prescriptive maintenance, simulation, decision-making, production planning and control, failure prediction
References
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Elbasheer, M., Longo, F., Mirabelli, G., Padovano, A., Soli-na, V., & Talarico, S. (2022). Integrated Prescriptive Maintenance and Production Planning: a Machine Learning Approach for the Development of an Autono-mous Decision Support Agent. IFAC-PapersOnLine, 55(10). https://doi.org/10.1016/j.ifacol.2022.10.102
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Maaten, L. van der, & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. Retrieved from http://jmlr.org/papers/v9/vandermaaten08a.html
Medjaher, K., Zerhouni, N., & Baklouti, J. (2013). Data-driven prognostics based on health indicator construc-tion: Application to PRONOSTIA's data. In European Control Conference (ECC).
Meissner, R., Meyer, H., & Wicke, K. (2021). Concept and Economic Evaluation of Prescriptive Maintenance Strat-egies for an Automated Condition Monitoring System. International Journal of Prognostics and Health Man-agement, 12(3). https://doi.org/10.36001/ijphm.2021.v12i3.2911
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in industry 4.0. Reliability Engineering & System Safety, 234, 109204. https://doi.org/10.1016/j.ress.2023.109204
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remain-ing useful life prediction. Mechanical Systems and Sig-nal Processing, 52-53(1), 228–247. https://doi.org/10.1016/j.ymssp.2014.05.029
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evalu-ating performance of prognostic techniques. In 2008 In-ternational Conference on Prognostics and Health Management, PHM 2008.
Schmidt, M., & Schäfers, P. (2017). The Hanoverian Supply Chain Model: modelling the impact of production plan-ning and control on a supply chain’s logistic objectives. Production Engineering, 11(4-5), 487–493. https://doi.org/10.1007/s11740-017-0740-9
Sridhar, S., & Sanagavarapu, S. (2021). Handling Data Imbalance in Predictive Maintenance for Machines using SMOTE-based Oversampling. In 2021 13th Internation-al Conference on Computational Intelligence and Com-munication Networks (CICN) (pp. 44–49). IEEE. https://doi.org/10.1109/CICN51697.2021.9574668
Teubert, C., Jarvis, K., Corbetta, M., Kulkarni, C., & Dai-gle, M. (2023). ProgPy v1.5 [Computer software]. Ze-nodo: Zenodo.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similarity-based prognostics approach for remaining useful life es-timation of engineered systems. In 2008 International Conference on Prognostics and Health Management, PHM 2008 (pp. 1–6). https://doi.org/10.1109/PHM.2008.4711421
Wesendrup, K., & Hellingrath, B. (2023). Post-prognostics demand management, production, spare parts and maintenance planning for a single-machine system using Reinforcement Learning. Computers & Industrial Engi-neering, 179, 109216. https://doi.org/10.1016/j.cie.2023.109216
Yang, J., Zhao, X., & Han, M. (2022). Joint optimization of imperfect condition-based maintenance and lot sizing via an availability-cost hybrid factor. Engineering Reports, 4(2). https://doi.org/10.1002/eng2.12462
Zarte, M., Wunder, U., & Pechmann, A. (2017). Concept and first case study for a generic predictive maintenance simulation in AnyLogic™. In IECON 2017 - 43rd An-nual Conference of the IEEE Industrial Electronics So-ciety.
Zhai, S., Gehring, B., & Reinhart, G. (2021). Enabling pre-dictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. Journal of Manufacturing Systems. Ad-vance online publication. https://doi.org/10.1016/j.jmsy.2021.02.006
Zhai, S., Riess, A., & Reinhart, G. (2019). Formulation and solution for the predictive maintenance integrated job shop scheduling problem. In 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019.
Zheng, R., Zhou, Y., Gu, L., & Zhang, Z. (2021). Joint optimization of lot sizing and condition-based mainte-nance for a production system using the proportional hazards model. Computers and Industrial Engineering, 154. https://doi.org/10.1016/j.cie.2021.107157
Broek, M. A. J. uit het, Teunter, R. H., Jonge, B. de, & Veldman, J. (2021). Joint condition-based maintenance and condition-based production optimization. Reliability Engineering and System Safety, 214. https://doi.org/10.1016/j.ress.2021.107743
Broek, M. A. J. uit het, Teunter, R. H., Jonge, B. de, Veld-man, J., & van Foreest, N. D. (2020). Condition-Based Production Planning: Adjusting Production Rates to Balance Output and Failure Risk. Manufacturing & Ser-vice Operations Management, 22(4), 792–811. https://doi.org/10.1287/msom.2019.0773
Cadavid, J. P. U., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in produc-tion planning and control: a state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531–1558. https://doi.org/10.1007/s10845-019-01531-7
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmey-er, W. P. (2011). SMOTE: Synthetic Minority Over-sampling Technique. Advance online publication. https://doi.org/10.48550/arXiv.1106.1813
Coffman, E. G., Garey, M. R., & Johnson, D. S. (1984). Approximation Algorithms for Bin-Packing — An Up-dated Survey. In G. Ausiello, M. Lucertini, & P. Serafini (Eds.), CISM International Centre for Mechanical Sci-ences. Algorithm Design for Computer System Design (Vol. 284, pp. 49–106). Vienna: Springer Vienna. https://doi.org/10.1007/978-3-7091-4338-4_3
Dehghan, H. S., Nourelfath, M., & Hajji, A. (2023). A deep learning approach for integrated production planning and predictive maintenance. International Journal of Pro-duction Research. Advance online publication. https://doi.org/10.1080/00207543.2022.2162618
Divya, D., Marath, B., & Santosh Kumar, M. B. (2023). Review of fault detection techniques for predictive maintenance. Journal of Quality in Maintenance Engi-neering, 29(2), 420–441. https://doi.org/10.1108/JQME-10-2020-0107
Elbasheer, M., Longo, F., Mirabelli, G., Padovano, A., Soli-na, V., & Talarico, S. (2022). Integrated Prescriptive Maintenance and Production Planning: a Machine Learning Approach for the Development of an Autono-mous Decision Support Agent. IFAC-PapersOnLine, 55(10). https://doi.org/10.1016/j.ifacol.2022.10.102
Fitouri, C., Fnaiech, N., Varnier, C., Fnaiech, F., & Zer-houni, N. (2016). A Decision-Making Approach for Job Shop Scheduling with Job Depending Degradation and Predictive Maintenance. IFAC-PapersOnLine, 49(12), 1490–1495. https://doi.org/10.1016/j.ifacol.2016.07.782
Goby, N., Brandt, T., & Neumann, D. (2023). Deep rein-forcement learning with combinatorial actions spaces: An application to prescriptive maintenance. Computers & Industrial Engineering, 179, 109165. https://doi.org/10.1016/j.cie.2023.109165
Guillén, A. J., Crespo, A., Macchi, M., & Gómez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning and Control, 27(12), 991–1004. https://doi.org/10.1080/09537287.2016.1171920
Jia, X., Huang, B., Feng, J., Cai, H., & Lee, J. (2018). A Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics. In 2018 Annual Confer-ence of the Prognostics and Health Management Socie-ty.
Kiran, D. R. (2019). Production Planning and Control: A Comprehensive Approach (1st ed.). Oxford: Butter-worth-Heinemann.
Ladj, A., Tayeb, F. B.‑S., Varnier, C., Dridi, A. A., & Selmane, N. (2017). A Hybrid of Variable Neighbor Search and Fuzzy Logic for the permutation flowshop scheduling problem with predictive maintenance. Proce-dia Computer Science, 112, 663–672. https://doi.org/10.1016/j.procs.2017.08.120
Ladj, A., Varnier, C., Tayeb, F. B.‑S., & Zerhouni, N. (2017). Exact and heuristic algorithms for post prognos-tic decision in a single multifunctional machine. Interna-tional Journal of Prognostics and Health Management, 8(2).
Laguna, M., & Marklund, J. (2018). Business Process Mod-eling, Simulation and Design. Boca Raton, FL: Chap-man and Hall / CRC. https://doi.org/10.1201/9781315162119
Liang, Z., Zhang, L., & Wang, X. (2023). A Novel Intelli-gent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost. Algorithms, 16(2), 98. https://doi.org/10.3390/a16020098
Liu, Y.‑Y., Chang, K.‑H., & Chen, Y.‑Y. (2023). Simulta-neous predictive maintenance and inventory policy in a continuously monitoring system using simulation opti-mization. Computers and Operations Research, 153. https://doi.org/10.1016/j.cor.2023.106146
Maaten, L. van der, & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. Retrieved from http://jmlr.org/papers/v9/vandermaaten08a.html
Medjaher, K., Zerhouni, N., & Baklouti, J. (2013). Data-driven prognostics based on health indicator construc-tion: Application to PRONOSTIA's data. In European Control Conference (ECC).
Meissner, R., Meyer, H., & Wicke, K. (2021). Concept and Economic Evaluation of Prescriptive Maintenance Strat-egies for an Automated Condition Monitoring System. International Journal of Prognostics and Health Man-agement, 12(3). https://doi.org/10.36001/ijphm.2021.v12i3.2911
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in industry 4.0. Reliability Engineering & System Safety, 234, 109204. https://doi.org/10.1016/j.ress.2023.109204
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remain-ing useful life prediction. Mechanical Systems and Sig-nal Processing, 52-53(1), 228–247. https://doi.org/10.1016/j.ymssp.2014.05.029
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evalu-ating performance of prognostic techniques. In 2008 In-ternational Conference on Prognostics and Health Management, PHM 2008.
Schmidt, M., & Schäfers, P. (2017). The Hanoverian Supply Chain Model: modelling the impact of production plan-ning and control on a supply chain’s logistic objectives. Production Engineering, 11(4-5), 487–493. https://doi.org/10.1007/s11740-017-0740-9
Sridhar, S., & Sanagavarapu, S. (2021). Handling Data Imbalance in Predictive Maintenance for Machines using SMOTE-based Oversampling. In 2021 13th Internation-al Conference on Computational Intelligence and Com-munication Networks (CICN) (pp. 44–49). IEEE. https://doi.org/10.1109/CICN51697.2021.9574668
Teubert, C., Jarvis, K., Corbetta, M., Kulkarni, C., & Dai-gle, M. (2023). ProgPy v1.5 [Computer software]. Ze-nodo: Zenodo.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similarity-based prognostics approach for remaining useful life es-timation of engineered systems. In 2008 International Conference on Prognostics and Health Management, PHM 2008 (pp. 1–6). https://doi.org/10.1109/PHM.2008.4711421
Wesendrup, K., & Hellingrath, B. (2023). Post-prognostics demand management, production, spare parts and maintenance planning for a single-machine system using Reinforcement Learning. Computers & Industrial Engi-neering, 179, 109216. https://doi.org/10.1016/j.cie.2023.109216
Yang, J., Zhao, X., & Han, M. (2022). Joint optimization of imperfect condition-based maintenance and lot sizing via an availability-cost hybrid factor. Engineering Reports, 4(2). https://doi.org/10.1002/eng2.12462
Zarte, M., Wunder, U., & Pechmann, A. (2017). Concept and first case study for a generic predictive maintenance simulation in AnyLogic™. In IECON 2017 - 43rd An-nual Conference of the IEEE Industrial Electronics So-ciety.
Zhai, S., Gehring, B., & Reinhart, G. (2021). Enabling pre-dictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. Journal of Manufacturing Systems. Ad-vance online publication. https://doi.org/10.1016/j.jmsy.2021.02.006
Zhai, S., Riess, A., & Reinhart, G. (2019). Formulation and solution for the predictive maintenance integrated job shop scheduling problem. In 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019.
Zheng, R., Zhou, Y., Gu, L., & Zhang, Z. (2021). Joint optimization of lot sizing and condition-based mainte-nance for a production system using the proportional hazards model. Computers and Industrial Engineering, 154. https://doi.org/10.1016/j.cie.2021.107157
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Technical Papers