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System-level failure prognostics: Literature review and main challenges

Author

Listed:
  • Ferhat Tamssaouet
  • Khanh TP Nguyen
  • Kamal Medjaher
  • Marcos Eduardo Orchard

Abstract

This paper reviews methods and practices for addressing the concepts of system-level prognostics (SLP) and system remaining useful life (SRUL) estimation applied to multicomponent systems. A precise definition of SLP is provided, emphasizing the advantages of its use in terms of identifying the scope of SLP applications. In addition, a comprehensive review of the literature is provided to properly classify and compare the findings of previously published studies in the field of SLP and evaluate the effectiveness of the available methodologies within the different stages of prognostic development. Finally, and considering that SLP is still a relatively recent research field, we also provide a thorough discussion on the main challenges that remain to be solved before achieving complete technology transfer, as well as future research directions.

Suggested Citation

  • Ferhat Tamssaouet & Khanh TP Nguyen & Kamal Medjaher & Marcos Eduardo Orchard, 2023. "System-level failure prognostics: Literature review and main challenges," Journal of Risk and Reliability, , vol. 237(3), pages 524-545, June.
  • Handle: RePEc:sae:risrel:v:237:y:2023:i:3:p:524-545
    DOI: 10.1177/1748006X221118448
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    References listed on IDEAS

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    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. F. Tamssaouet & S. Amari, 2018. "Modelling and temporal evaluation of networked control systems using timed automata with guards and (max,+) algebra," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(10), pages 2073-2088, July.
    3. John J. McCall, 1965. "Maintenance Policies for Stochastically Failing Equipment: A Survey," Management Science, INFORMS, vol. 11(5), pages 493-524, March.
    4. Khorasgani, Hamed & Biswas, Gautam & Sankararaman, Shankar, 2016. "Methodologies for system-level remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 8-18.
    5. Wenshuo Tang & Darius Roman & Ross Dickie & Valentin Robu & David Flynn, 2020. "Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems," Energies, MDPI, vol. 13(18), pages 1-29, September.
    6. Linkan Bian & Nagi Gebraeel, 2014. "Stochastic framework for partially degradation systems with continuous component degradation‐rate‐interactions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(4), pages 286-303, June.
    7. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    8. 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.
    9. Qianhui Wu & Keqin Ding & Biqing Huang, 2020. "Approach for fault prognosis using recurrent neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1621-1633, October.
    10. Niu, Gang & Jiang, Junjie, 2017. "Prognostic control-enhanced maintenance optimization for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 218-226.
    11. Mengyao Gu & Youling Chen, 2019. "Two improvements of similarity-based residual life prediction methods," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 303-315, January.
    12. Cho, Danny I. & Parlar, Mahmut, 1991. "A survey of maintenance models for multi-unit systems," European Journal of Operational Research, Elsevier, vol. 51(1), pages 1-23, March.
    13. Nguyen, T.P. Khanh & Yeung, Thomas G. & Castanier, Bruno, 2013. "Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories," International Journal of Production Economics, Elsevier, vol. 143(2), pages 472-477.
    14. Rommert Dekker & Ralph Wildeman & Frank Duyn Schouten, 1997. "A review of multi-component maintenance models with economic dependence," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 45(3), pages 411-435, October.
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