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Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking

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  • Huynh, K.T.
  • Grall, A.
  • Bérenguer, C.

Abstract

Seeking condition indices characterizing the health state of a system is a key problem in condition-based maintenance. For this purpose, diagnostic and prognostic models have been unceasingly developed and improved over the past few decades; nevertheless none of them explains thoroughly the impacts of such indices on the effectiveness of maintenance operations. As a complement to these efforts, this paper analyzes the effectiveness of some well-known diagnostic and prognostic indices for maintenance decision-making. The study is based on a system subject to competing risks due to multiple crack paths. A periodic inspection scheme is used to monitor the system health state. Each inspection returns the perfect diagnostic information: the number of cracks, corresponding crack sizes, and the system failure/working state. Based on this information, two kinds of prognostic condition indices are predicted: the average value and probability law of the system residual useful life. The associated condition-based maintenance strategies and cost models are then developed and compared with the ones whose maintenance decisions are based on diagnostic condition indices. The comparison results allow us to conclude on the performance and on the robustness of these strategies, hence giving some suggestions on the choice of reliable condition indices for maintenance decision-making.

Suggested Citation

  • Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:237-254
    DOI: 10.1016/j.ress.2016.11.022
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    Cited by:

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    3. Bautista, Lucía & Castro, Inma T. & Landesa, Luis, 2022. "Condition-based maintenance for a system subject to multiple degradation processes with stochastic arrival intensity," European Journal of Operational Research, Elsevier, vol. 302(2), pages 560-574.
    4. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    5. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    8. Huynh, K.T. & Vu, H.C. & Nguyen, T.D. & Ho, A.C., 2022. "A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Khac Tuan Huynh & Antoine Grall, 2020. "A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems," Journal of Risk and Reliability, , vol. 234(2), pages 333-358, April.
    10. Zhang, Xiaohong & Liao, Haitao & Zeng, Jianchao & Shi, Guannan & Zhao, Bing, 2021. "Optimal Condition-based Opportunistic Maintenance and Spare Parts Provisioning for a Two-unit System using a State Space Partitioning Approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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