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Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components

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  • de Pater, Ingeborg
  • Mitici, Mihaela

Abstract

Aircraft maintenance is undergoing a paradigm shift towards predictive maintenance, where the use of sensor data and Remaining-Useful-Life prognostics are central. This paper proposes an integrated approach for predictive aircraft maintenance planning for multiple multi-component systems, where the components are repairables. First, model-based Remaining-Useful-Life prognostics are developed. These prognostics are updated over time, as more sensor data become available. Then, a rolling horizon integer linear program is developed for the maintenance planning of multiple multi-component systems. This model integrates the Remaining-Useful-Life prognostics with the management of a limited stock of spare repairable components. The maintenance of the multiple systems is linked through the availability of spare components and shared maintenance time slots. Our approach is illustrated for a fleet of aircraft, each equipped with a Cooling System consisting of four Cooling Units. For an aircraft to be operational, a minimum of two Cooling Units out of the four need to be operational. The maintenance planning results show that our integrated approach reduces the costs with maintenance by 48% relative to a corrective maintenance strategy and by 30% relative to a preventive maintenance strategy. Moreover, using predictive maintenance, components are replaced in anticipation of failure without wasting their useful life. In general, our approach provides a roadmap from Remaining-Useful-Life prognostics to maintenance planning for multiple multi-component systems of repairables with a limited stock of spares.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s095183202100288x
    DOI: 10.1016/j.ress.2021.107761
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    as
    1. 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.
    2. Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Mercier, Sophie & Pham, Hai Ha, 2012. "A preventive maintenance policy for a continuously monitored system with correlated wear indicators," European Journal of Operational Research, Elsevier, vol. 222(2), pages 263-272.
    4. Liang, Zhenglin & Parlikad, Ajith Kumar, 2020. "Predictive group maintenance for multi-system multi-component networks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Jiao, Ruihua & Peng, Kaixiang & Dong, Jie & Zhang, Chuanfang, 2020. "Fault monitoring and remaining useful life prediction framework for multiple fault modes in prognostics," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Van Horenbeek, Adriaan & Pintelon, Liliane, 2013. "A dynamic predictive maintenance policy for complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 39-50.
    7. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Wang, Ling & Chu, Jian & Mao, Weijie, 2009. "A condition-based replacement and spare provisioning policy for deteriorating systems with uncertain deterioration to failure," European Journal of Operational Research, Elsevier, vol. 194(1), pages 184-205, April.
    9. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 214-224.
    10. Sheng‐Tsaing Tseng & Jen Tang & In‐Hong Ku, 2003. "Determination of burn‐in parameters and residual life for highly reliable products," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(1), pages 1-14, February.
    11. Olde Keizer, Minou C.A. & Flapper, Simme Douwe P. & Teunter, Ruud H., 2017. "Condition-based maintenance policies for systems with multiple dependent components: A review," European Journal of Operational Research, Elsevier, vol. 261(2), pages 405-420.
    12. Nan Chen & Kwok Tsui, 2013. "Condition monitoring and remaining useful life prediction using degradation signals: revisited," IISE Transactions, Taylor & Francis Journals, vol. 45(9), pages 939-952.
    13. Alaa H. Elwany & Nagi Z. Gebraeel & Lisa M. Maillart, 2011. "Structured Replacement Policies for Components with Complex Degradation Processes and Dedicated Sensors," Operations Research, INFORMS, vol. 59(3), pages 684-695, June.
    14. Bouvard, K. & Artus, S. & Bérenguer, C. & Cocquempot, V., 2011. "Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 601-610.
    15. Zhuoqi Zhang & Su Wu & Binfeng Li & Seungchul Lee, 2015. "(, ) type maintenance policy for multi-component systems with failure interactions," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1051-1064, April.
    16. Si, Xiao-Sheng & Wang, Wenbin & Chen, Mao-Yin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution," European Journal of Operational Research, Elsevier, vol. 226(1), pages 53-66.
    17. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 202-213.
    18. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    19. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    20. Chen, Dongyan & Trivedi, Kishor S., 2005. "Optimization for condition-based maintenance with semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 25-29.
    21. de Smidt-Destombes, Karin S. & van der Heijden, Matthieu C. & van Harten, Aart, 2009. "Joint optimisation of spare part inventory, maintenance frequency and repair capacity for k-out-of-N systems," International Journal of Production Economics, Elsevier, vol. 118(1), pages 260-268, March.
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    Cited by:

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