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Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection

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  • Zhang, Wei-Heng
  • Qin, Jianjun
  • Lu, Da-Gang
  • Liu, Min
  • Faber, Michael H.

Abstract

Condition monitoring systems (CMSs) can be used to further reduce the expected value of life-cycle cost in the context of risk-based inspection (RBI) planning. However, the degradation of CMS monitoring performance and the utilization of historical data lead to the time-varying property of CMS monitoring performance, which would significantly affect the contributions of CMS. To facilitate risk analysis, a stochastic degradation model and Bayesian theorem are utilized to model the time-varying monitoring performance. Furthermore, a selection method of the CMS identification threshold is proposed to improve the CMS contributions to RBI planning further. To quantify the CMS contributions from a cost-effective perspective, this paper proposes an analytical framework on the value of CMS information analysis, which integrates condition-based maintenance action into RBI planning. In this framework, the importance of considering the time-varying performance of CMS can also be quantified by value of information (VoI) analysis. A case study of fatigue-induced degradation of a welded connection is used to clarify the proposed framework, in which the value of CMS information is quantified and the importance of considering time-varying monitoring performance is highlighted. Finally, the optimal implementation strategy regarding the CMS operation period is identified based on the VoI metric.

Suggested Citation

  • Zhang, Wei-Heng & Qin, Jianjun & Lu, Da-Gang & Liu, Min & Faber, Michael H., 2023. "Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006081
    DOI: 10.1016/j.ress.2022.108993
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    References listed on IDEAS

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    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Hu, Jiawen & Chen, Piao, 2020. "Predictive maintenance of systems subject to hard failure based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    3. Maria Rosaria Termite & Piero Baraldi & Sameer Al-Dahidi & Luca Bellani & Michele Compare & Enrico Zio, 2019. "A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments," Energies, MDPI, vol. 12(24), pages 1-26, December.
    4. 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.
    5. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. 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).
    7. Yuan, Xian-Xun & Higo, Eishiro & Pandey, Mahesh D., 2021. "Estimation of the value of an inspection and maintenance program: A Bayesian gamma process model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Lin, Chaochao & Song, Junho & Pozzi, Matteo, 2022. "Optimal inspection of binary systems via Value of Information analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    9. Zhi‐Sheng Ye & Min Xie, 2015. "Stochastic modelling and analysis of degradation for highly reliable products," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 16-32, January.
    10. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan & Kim, Sooho, 2019. "Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 181-192.
    12. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
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