Fault Detection and Isolation in System of Multiple-Sources of Energy Using Hierarchical Bayesian Belief Networks

Abstract

Abstract Ensuring fault tolerance in Systems of Multiple-Sources of Energy (SMSE) is of paramount impor-tance for their reliable operation. Detecting, localizing, and characterizing faults are critical tasks toensure system integrity. One crucial element within SMSE, susceptible to faults, is the power convert-ers. This paper introduces an innovative approach leveraging Hierarchical Bayesian Belief Networks(HBBNs) for the identification and isolation of open circuit faults in DC-DC power converters com-monly employed in SMSE applications. Our proposed method addresses the challenge of fault detectionand localization, contributing to enhanced system reliability. The effectiveness of the approach isdemonstrated through extensive testing on simulated data generated via a developed state spacemodel. The results underscore the viability of our approach in bolstering the robustness of DC-DCpower converters against open circuit faults.