Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

Abstract

Ensuring fault tolerance in Systems of Multiple-Sources of Energy (SMSE) is of paramount importance for their reliable operation. Detecting, localizing, and characterizing faults are critical tasks to ensure system integrity. One crucial element within SMSE, susceptible to faults, is the power converters. 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 commonly employed in SMSE applications. Our proposed method addresses the challenge of fault detection and localization, contributing to enhanced system reliability. The effectiveness of the approach is demonstrated through extensive testing on simulated data generated via a developed state space model. The results underscore the viability of our approach in bolstering the robustness of DC-DC power converters against open circuit faults.