IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v237y2023ics0951832023002715.html
   My bibliography  Save this article

Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost

Author

Listed:
  • Zhou, Kai-Li
  • Cheng, De-Jun
  • Zhang, Han-Bing
  • Hu, Zhong-tai
  • Zhang, Chun-Yan

Abstract

Due to the increase in the series-parallel multi-state system (MSS) complexity caused by the nonlinear change of parameters, the traditional model-based maintenance methods are becoming less effective and obsolete. This study proposes a novel deep learning-based intelligent multilevel predictive maintenance (MPM) framework for series-parallel MSS considering comprehensive cost. A new adaptive convolution-transformer (C-Transformer) was constructed to predict component remaining useful life (RUL) through extracting features adaptively. Based on this, the component failure probability was obtained through convolutional neural network (CNN). Then, to directly reflect the operating conditions of MSSs, multilevel maintenance was customized with multilevel failure through the trial-and-error learning method. During the intermission breaks, an intelligent dynamic decision-making optimization model was proposed by introducing multilevel maintenance to improve the system's state in a future mission, which was solved by a new artificial bee colony algorithm (called MDU-ABC-K) to minimize the comprehensive cost under economic dependence and critical component constraints, thus simultaneously balancing maintenance time and cost. The proposed approach was compared with other models through turbofan engine data set by NASA. The comparison results indicate that the proposed intelligent MPM framework can offer a more reasonable and superior maintenance strategy.

Suggested Citation

  • Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002715
    DOI: 10.1016/j.ress.2023.109357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023002715
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109357?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Tao & Liu, Yu, 2020. "Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    3. Dao, Cuong D. & Zuo, Ming J., 2017. "Optimal selective maintenance for multi-state systems in variable loading conditions," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 171-180.
    4. Hesabi, Hadis & Nourelfath, Mustapha & Hajji, Adnène, 2022. "A deep learning predictive model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Nourelfath, Mustapha & Châtelet, Eric, 2012. "Integrating production, inventory and maintenance planning for a parallel system with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 59-66.
    7. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    9. Pan, Tongyang & Chen, Jinglong & Ye, Zhisheng & Li, Aimin, 2022. "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Pandey, Mayank & Zuo, Ming J. & Moghaddass, Ramin & Tiwari, M.K., 2013. "Selective maintenance for binary systems under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 42-51.
    11. Mo, Yuchang & Xing, Liudong & Cui, Lirong & Si, Shubin, 2017. "MDD-based performability analysis of multi-state linear consecutive-k-out-of-n: F systems," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 124-131.
    12. Mo, Yuchang & Liu, Yu & Cui, Lirong, 2018. "Performability analysis of multi-state series-parallel systems with heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 48-56.
    13. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Dao, Cuong D. & Zuo, Ming J., 2017. "Selective maintenance of multi-state systems with structural dependence," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 184-195.
    16. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    17. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    18. Alsyouf, Imad, 2007. "The role of maintenance in improving companies' productivity and profitability," International Journal of Production Economics, Elsevier, vol. 105(1), pages 70-78, January.
    19. Chaabane, K. & Khatab, A. & Diallo, C. & Aghezzaf, E.-H. & Venkatadri, U., 2020. "Integrated imperfect multimission selective maintenance and repairpersons assignment problem," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    20. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    21. Dao, Cuong D. & Zuo, Ming J. & Pandey, Mayank, 2014. "Selective maintenance for multi-state series–parallel systems under economic dependence," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 240-249.
    22. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    23. Maaroufi, Ghofrane & Chelbi, Anis & Rezg, Nidhal, 2013. "Optimal selective renewal policy for systems subject to propagated failures with global effect and failure isolation phenomena," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 61-70.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    2. Chaabane, K. & Khatab, A. & Diallo, C. & Aghezzaf, E.-H. & Venkatadri, U., 2020. "Integrated imperfect multimission selective maintenance and repairpersons assignment problem," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Jiang, Tao & Liu, Yu, 2020. "Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Chen, Yiming & Liu, Yu & Jiang, Tao, 2021. "Optimal maintenance strategy for multi-state systems with single maintenance capacity and arbitrarily distributed maintenance time," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    5. Wenbin Cao & Xisheng Jia & Yu Liu & Qiwei Hu & Jianmin Zhao, 2019. "Selective maintenance optimisation considering random common cause failures and imperfect maintenance," Journal of Risk and Reliability, , vol. 233(3), pages 427-443, June.
    6. Liu, Lujie & Yang, Jun & Kong, Xuefeng & Xiao, Yiyong, 2022. "Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Diallo, Claver & Venkatadri, Uday & Khatab, Abdelhakim & Liu, Zhuojun, 2018. "Optimal selective maintenance decisions for large serial k-out-of-n: G systems under imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 234-245.
    9. A. Khatab & C. Diallo & E.-H. Aghezzaf & U. Venkatadri, 2022. "Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 703-718, March.
    10. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    11. Yin, Mingang & Liu, Yu & Liu, Shuntao & Chen, Yiming & Yan, Yutao, 2023. "Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    12. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2024. "Stochastic programming for selective maintenance optimization with uncertainty in the next mission conditions," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    13. Xiaosheng Zhang & Jianqiao Chen & Ben Han & Junxiang Li, 2019. "Multi-mission selective maintenance modelling for multistate systems over a finite time horizon," Journal of Risk and Reliability, , vol. 233(6), pages 1040-1059, December.
    14. Shahraki, Ameneh Forouzandeh & Yadav, Om Prakash & Vogiatzis, Chrysafis, 2020. "Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    15. Xisheng Jia & Wenbin Cao & Qiwei Hu, 2019. "Selective maintenance optimization for random phased-mission systems subject to random common cause failures," Journal of Risk and Reliability, , vol. 233(3), pages 379-400, June.
    16. Ma, Weining & Zhang, Qin & Xiahou, Tangfan & Liu, Yu & Jia, Xisheng, 2023. "Integrated selective maintenance and task assignment optimization for multi-state systems executing multiple missions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    18. Shen, Jingyuan & Hu, Jiawen & Ma, Yizhong, 2020. "Two preventive replacement strategies for systems with protective auxiliary parts subject to degradation and economic dependence," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    19. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    20. Hamzea Al-Jabouri & Ahmed Saif & Claver Diallo, 2023. "Robust selective maintenance optimization of series–parallel mission-critical systems subject to maintenance quality uncertainty," Computational Management Science, Springer, vol. 20(1), pages 1-31, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.