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Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems

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  • Nguyen, Van-Thai
  • Do, Phuc
  • Vosin, Alexandre
  • Iung, Benoit

Abstract

Currently, in manufacturing, massive useful data about health condition and maintenance is often available thanks to Industry 4.0 technologies. However, how to take advantage of historical data to optimize maintenance policies for multi-component systems has still been a challenging problem. This is especially true when maintenance cost models at component level are not available and/or maintenance actions are imperfect. In order to cope with this issue, we propose in this paper an artificial-intelligence-based maintenance approach which first constructs a predictor based on artificial neural network (ANN) for estimating maintenance cost at system level and then employs a customized multi-agent deep reinforcement learning algorithm to optimize maintenance decisions that can be applied for large-scale systems. To evaluate the performance and scalability of the proposed maintenance approach, numerical studies are conducted on a small 4-component system with different configurations and a large system composed of 15 components considering both deterministic and random maintenance quality. The simulation results show that ANN-based predictor is efficient for maintenance cost forecasting and multi-agent deep reinforcement learning is a promising solution for maintenance decision-making and optimization.

Suggested Citation

  • Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022003805
    DOI: 10.1016/j.ress.2022.108757
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    References listed on IDEAS

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    1. Castro, I.T., 2009. "A model of imperfect preventive maintenance with dependent failure modes," European Journal of Operational Research, Elsevier, vol. 196(1), pages 217-224, July.
    2. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    3. Robin P. Nicolai & Rommert Dekker, 2008. "Optimal Maintenance of Multi-component Systems: A Review," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 11, pages 263-286, Springer.
    4. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    5. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    6. Zhang, Nailong & Si, Wujun, 2020. "Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
    8. 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).
    9. 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.
    10. Do, Phuc & Assaf, Roy & Scarf, Phil & Iung, Benoit, 2019. "Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 86-97.
    11. Do, Phuc & Voisin, Alexandre & Levrat, Eric & Iung, Benoit, 2015. "A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 22-32.
    12. Andriotis, C.P. & Papakonstantinou, K.G., 2021. "Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
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