Bayesian Network-Based Fault Diagnostic System for Nuclear Power Plant Assets

Bayesian Network-Based Fault Diagnostic System for Nuclear Power Plant Assets

Presented at the 2023 BayesiaLab Conference.


Future advances in nuclear power technologies call for enhanced operator advice and autonomous control capabilities. One of the first tasks in developing such capabilities is the formulation of symptom-based conditional failure probabilities for power plant structures, systems, and components (SSCs). The primary goal is to aid plant personnel in (1) deducing the probabilistic performance status of the monitored SSCs and (2) detecting impending faults/failures. The task of estimating conditional failure probability is a bidirectional inference problem, and a logical approach is to use the Bayesian network (BN) method. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers reasoning capability under uncertainty, graphical representation emulating the physical behavior of the target SSC, and explainability of the model structure and results. This presentation will provide an overview of the BN technique and the software tools for implementing BN models in this task, along with the associated knowledge representation and reasoning paradigm. The challenges with data availability and the general approach to target SSC identification will be highlighted. Two example case studies on the failure of (1) a centrifugal pump and (2) an electric motor will be presented to demonstrate the usefulness and technical feasibility of the proposed BN-based fault diagnostic artificial reasoning system using expert system shells.

Presentation Video

Presentation Slides

About the Presenter

Dr. Xingang Zhao is an R&D Scientist at Oak Ridge National Laboratory. He received his Ph.D. in nuclear science and engineering from the Massachusetts Institute of Technology. His research interests span multiple disciplines of clean energy systems and their intersections with artificial intelligence and decision science. He has been a major contributor to a diverse portfolio of research projects that advance the state of the art of modeling & simulation and digital engineering for nuclear and renewable energy applications.

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