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System dynamic reliability assessment and failure prognostics

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  • Liu, Jie
  • Zio, Enrico

Abstract

Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.

Suggested Citation

  • Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
    • Handle: RePEc:eee:reensy:v:160:y:2017:i:c:p:21-36
    DOI: 10.1016/j.ress.2016.12.003
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    References listed on IDEAS

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    Cited by:

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    6. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Chiacchio, Ferdinando & Iacono, Alessandra & Compagno, Lucio & D'Urso, Diego, 2020. "A general framework for dependability modelling coupling discrete-event and time-driven simulation," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    8. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
    9. 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).
    10. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    12. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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