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A deep learning predictive model for selective maintenance optimization

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  • Hesabi, Hadis
  • Nourelfath, Mustapha
  • Hajji, Adnène

Abstract

This paper develops a predictive selective maintenance framework using deep learning and mathematical programming. We consider a multi-component system executing consecutive production missions with scheduled intermission maintenance breaks. During the intermission breaks, several maintenance actions can improve each component's remaining useful life at a given cost. An optimization model is developed to identify a subset of maintenance actions to perform on the components. The objective is to minimize the total cost under intermission break time limitation. The total cost is composed of maintenance and failure costs; it depends on the success probabilities of the subsequent missions. To estimate these probabilities, the optimization model interacts with a long short-term memory network. The resulting predictive selective maintenance framework is validated using a benchmarking data set provided by NASA for a Modular Aero-Propulsion System Simulation of a Commercial Turbofan Engine. Its performance is highlighted when compared with the model-based approach. The results illustrate the advantages of the predictive selective maintenance framework to predict the health condition of each component with accuracy and deal with the selective maintenance of series systems.

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  • 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).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s095183202100675x
    DOI: 10.1016/j.ress.2021.108191
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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
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    4. Khatab, A. & Aghezzaf, E.-H., 2016. "Selective maintenance optimization when quality of imperfect maintenance actions are stochastic," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 182-189.
    5. Han, Xiao & Wang, Zili & Xie, Min & He, Yihai & Li, Yao & Wang, Wenzhuo, 2021. "Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    6. Mayank Pandey & Ming Zuo & Ramin Moghaddass, 2013. "Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance," IISE Transactions, Taylor & Francis Journals, vol. 45(11), pages 1221-1234.
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    1. 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).
    2. Liao, Ruoyu & He, Yihai & Feng, Tianyu & Yang, Xiuzhen & Dai, Wei & Zhang, Weifang, 2023. "Mission reliability-driven risk-based predictive maintenance approach of multistate manufacturing system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Pedro Nunes & Eugénio Rocha & José Santos, 2024. "Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation," Future Internet, MDPI, vol. 16(6), pages 1-17, June.
    5. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    6. Dui, Hongyan & Liu, Meng & Song, Jiaying & Wu, Shaomin, 2023. "Importance measure-based resilience management: Review, methodology and perspectives on maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. 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).
    8. Carlon, André Gustavo & Kroetz, Henrique Machado & Torii, André Jacomel & Lopez, Rafael Holdorf & Miguel, Leandro Fleck Fadel, 2022. "Risk optimization using the Chernoff bound and stochastic gradient descent," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    9. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. 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).
    11. Zhang, Lin & Chen, Xiaohui & Khatab, Abdelhakim & An, Youjun & Feng, XiaoNing, 2024. "Joint optimization of selective maintenance and repairpersons assignment problem for mission-oriented systems operating under s-dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. Li, Yaping & Xia, Tangbin & Chen, Zhen & Pan, Ershun, 2023. "Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. 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).
    14. 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).

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