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Predictive group maintenance for multi-system multi-component networks

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  • Liang, Zhenglin
  • Parlikad, Ajith Kumar

Abstract

Predictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi-components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model's practical significance.

Suggested Citation

  • Liang, Zhenglin & Parlikad, Ajith Kumar, 2020. "Predictive group maintenance for multi-system multi-component networks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832018311633
    DOI: 10.1016/j.ress.2019.106704
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. Pedersen, Tom Ivar & Vatn, Jørn, 2022. "Optimizing a condition-based maintenance policy by taking the preferences of a risk-averse decision maker into account," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Manco, Pasquale & Rinaldi, Marta & Caterino, Mario & Fera, Marcello & Macchiaroli, Roberto, 2022. "Maintenance management for geographically distributed assets: a criticality-based approach," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    6. Zhang, Nan & Cai, Kaiquan & Deng, Yingjun & Zhang, Jun, 2023. "Determining the optimal production–maintenance policy of a parallel production system with stochastically interacted yield and deterioration," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Dinh, Duc-Hanh & Do, Phuc & Iung, Benoit, 2022. "Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Urbani, Michele & Brunelli, Matteo & Punkka, Antti, 2023. "An approach for bi-objective maintenance scheduling on a networked system with limited resources," European Journal of Operational Research, Elsevier, vol. 305(1), pages 101-113.
    9. Xia, Tangbin & Cao, Lei & Xu, Yuhui & Zhang, Kaigan & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2024. "Multi-level maintenance and inventory joint optimization for a k-out-of-n hyper-system considering the selection of suppliers with incentive discount policies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    12. Jingyi Zhao & Chunhai Gao & Tao Tang, 2022. "A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment," Sustainability, MDPI, vol. 14(5), pages 1-22, March.
    13. Wang, Yukun & Li, Xiaopeng & Chen, Junyan & Liu, Yiliu, 2022. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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