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

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.