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A framework for dynamic risk assessment with condition monitoring data and inspection data

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  • Xing, Jinduo
  • Zeng, Zhiguo
  • Zio, Enrico

Abstract

In this paper, a framework is proposed for integrating condition monitoring and inspection data in Dynamic risk assessment (DRA). Condition monitoring data are online-collected by sensors and indirectly relate to component degradation; inspection data are recorded in physical inspections that directly measure the component degradation. A Hidden Markov Gaussian Mixture Model (HM-GMM) is developed for modeling the condition monitoring data and a Bayesian network (BN) is developed to integrate the two data sources for DRA. Risk updating and prediction are exemplified on an Event Tree (ET) risk assessment model. A numerical case study and a real-world application on a Nuclear Power Plant (NPP) are performed to demonstrate the application of the proposed framework.

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  • 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).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018305118
    DOI: 10.1016/j.ress.2019.106552
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