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

Jinduo Xing, Zhiguo Zeng and Enrico Zio

Reliability Engineering and System Safety, 2019, vol. 191, issue C

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.

Keywords: Dynamic risk assessment (DRA); Condition monitoring data; Inspection data; Hidden Markov Gaussian Mixture Model (HM-GMM); Bayesian network (BN); Probabilistic risk assessment (PRA); Prognostic and health management (PHM); Event tree (ET); Nuclear power plant (NPP) (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018305118

DOI: 10.1016/j.ress.2019.106552

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