Remaining Useful Life Prediction for a Multi-Component System with Degradation Interactions

Document Type : Original Research Article

Authors

Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

Abstract

Remaining useful life (RUL) prediction is crucial in prognostics and health management (PHM) systems. The primary objective is to forecast the time to failure (TTF) or anticipate the RUL of a system. In real industrial cases, systems typically consist of multiple components that can affect each other, and ignoring these dependencies when modeling PHM systems can lead to erroneous RUL predictions and ineffective maintenance planning. Recognizing this, the focus of this paper is on the prognostics of multi-component systems, where the degradation processes of the system are influenced by both internal factors specific to the components and external factors related to the environment.

Keywords

Main Subjects

References

  1. Dekker, RE. Wldeman and D. Schouten, “A review of multi-component maintenance models with economic dependence”. Mathematical Methods of Operations Research, vol. 45, pp. 411-435, 1997. doi: https://doi.org/10.1007/BF01194788
  2. P. Nicolai and R. Dekker, “Optimal Maintenance of Multi-component Systems: A Review”. Complex System Maintenance Handbook. pp. 263–286, 2008,doi: https://doi.org/10.1007/978-1-84800-011-7_11
  3. Cao, X. Jia, Q. Hu, J. Zhao and Y. Wu, “A literature review on selective maintenance for multi‐unit systems”. RESEARCH ARTICLE, vol. 34, pp. 824-845, 2018, doi: https://doi.org/10.1002/qre.2293
  4. Zhang, M. Chen and D. Zhou, "Predicting remaining useful life for a multi-Component system with public noise," 2016 Prognostics and System Health Management Conference (PHM-Chengdu), Chengdu, China, 2016, pp. 1-6, doi: https://doi.org/10.1109/PHM.2016.7819819
  5. Bian and N. Gebraeel “Stochastic Modeling and Real-Time Prognostics for Multi-Component Systems with Degradation-Rate-Interactions”. IIE Transactions, vol.46, pp. 470-482 2014. doi: https://doi.org/10.1080/0740817X.2013.812269
  6. -H. Sheu, T. -H. Liu, Z. G. Zhang and Y. -H. Chien, "Extended Optimal Replacement Policy for a Two-Unit System with Shock Damage Interaction", IEEE Transactions on Reliability, vol. 64, no. 3, pp. 998-1014, Sept. 2015, doi: https://doi.org/10.1109/TR.2015.2427231
  7. Zhang, M. Fouladirad and A. Barros “Optimal imperfect maintenance cost analysis of a two-component system with failure interactions”. Reliability Engineering and System Safety. Vol. 177, pp. 24-34, 2018, doi: https://doi.org/10.1016/j.ress.2018.04.019
  8. Dinh, P. Do and B. Iung “Degradation modeling and reliability assessment for a multi-component system with structural dependence”. Computers & Industrial Engineering, vol. 144, p. 106443, 2020, doi: https://doi.org/10.1016/j.cie.2020.106443
  9. Bakir, M. Yildirim and E. Ursavas, “An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance”. Renewable and Sustainable Energy Reviews, vol. 138, p. 110639, 2021, doi: https://doi.org/10.1016/j.rser.2020.110639
  10. Niu H, Zeng J, Shi H, Zhang X, Liang J. “Degradation Modeling and Remaining Useful Life Prediction for a Multicomponent System with Stochastic Dependence”. Computers & Industrial Engineering, vol. 175, p. 108889, 2022, doi: https://doi.org/10.1016/j.cie.2022.108889
  11. Özgür-Ünlüakın and B. Turkali “Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs”. Reliability Engineering and System Safety, vol. 211, p. 107559, 2021, doi: https://doi.org/10.1016/j.ress.2021.107559
  12. Zhang, K. Cai, J. Zhang and T. Wang, “A condition-based maintenance policy considering failure dependence and imperfect inspection for a two-component system”. Reliability Engineering and System Safety, vol. 217, p. 108069, 2022, doi: https://doi.org/10.1016/j.ress.2021.108069
  13. Nguyen, K. Medjaher and C. Gogu, “Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems”, Reliability Engineering and System Safety, vol. 222, p. 108383, 2022, doi: https://doi.org/10.1016/j.ress.2022.108383
  14. Li, W. Zhu, L. Dieulle and E. Deloux “Condition-based maintenance strategies for stochastically dependent systems using Nested Lévy copulas”, Reliability Engineering and System Safety, vol. 217, p. 108038, 2022, doi: https://doi.org/10.1016/j.ress.2021.108038
  15. Shi and J. Zeng “Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence”, Computers & Industrial Engineering, Vol. 93,  pp. 192-204, 2016, doi: https://doi.org/10.1016/j.cie.2015.12.016
  16. Zhang, and W. Si, “Deep Reinforcement Learning for Condition-Based Maintenance Planning of Multi-Component Systems Under Dependent Competing Risks”. Reliability Engineering and System Safety, vol. 203, p. 107094, 2020, doi: https://doi.org/10.1016/j.ress.2020.107094
  17. Shen, A. Elwany and L. Cui, “Reliability analysis for multi-component systems with degradation interaction and categorized shocks” Applied Mathematical Modelling, Vol. 56, pp. 487-500, 2018, doi: https://doi.org/10.1016/j.apm.2017.12.001
  18. Martinod, O. Bistorin, LF. Castaneda and N. Rezg, “Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions”. Computers & Industrial Engineering, Vol. 124, pp. 100-112, 2018, doi: https://doi.org/10.1016/j.cie.2018.07.019
  19. Xu, X. Jin, S. Kamarthi and Md. Noor-E-Alam “A failure-dependency modeling and state discretization approach for condition-based maintenance optimization of multi-component systems”. Journal of Manufacturing Systems, Vol. 47, pp. 141-152, 2018, doi: https://doi.org/10.1016/j.jmsy.2018.04.018
  20. Nguyen, P. Do and A. Grall “Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance”. Reliability Engineering and System Safety, Vol. 168, pp. 249-261, 2017, doi: https://doi.org/10.1016/j.ress.2017.05.034
  21. Do, R. Assef, P. Scarf and B. Lung “Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies”. Reliability Engineering and System Safety, vol. 189, pp.: 86-97, 2019. doi: https://doi.org/10.1016/j.ress.2018.10.007
  22. Shafiee, A. Labib, J. Maiti and A. Starr, “Maintenance strategy selection for multi-component systems using a combined analytic network process and cost-risk criticality model”. Institution MECHANICAL ENGINEERS, Vol. 233, no. 2, pp. 89-104, 2019, doi: https://doi.org/10.1177/1748006X17712071
  23. Liu, MD. Pandey, X. Wang and X. Zhao “A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes”. European Journal of Operational Research, Vol. 295, pp. 705-717, 2021, doi: https://doi.org/10.1016/j.ejor.2021.03.010
  24. Wang, X. Li, J. Chen and Y. Liu “A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies”. Reliability Engineering and System Safety, vol. 219, p. 108174, 2022, doi: https://doi.org/10.1016/j.ress.2021.108174
  25. Oakley, KJ. Wilson and P. Philipson. “A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence”. Reliability Engineering and System Safety, vol. 222, p. 108321, 2022, doi: https://doi.org/10.1016/j.ress.2022.108321
  26. Zhou, TR. Lin, Y. Sun and L. Ma, “Maintenance optimization of a parallel-series system with stochastic and economic dependence under limited maintenance capacity”. Reliability Engineering and System Safet, Vol. 155, pp. 137-146, 2016, doi: https://doi.org/10.1016/j.ress.2016.06.012
International Journal of Reliability, Risk and Safety: Theory and Application
Volume 6, Issue 1
July 2023
Pages 27-45

Files

History

  • Receive Date: 22 May 2023
  • Revise Date: 18 July 2023
  • Accept Date: 25 July 2023

Share

How to cite

Statistics