A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology. Note to Practitioners-This paper is motivated by the problem of fault diagnosis of complex electronic systems in the presence of transient fault (TF) and intermittent fault (IF). Existing approaches do not involve the dynamic behavior of electronic systems, i.e., degradation and aging. The results of fault diagnosis could be different at different times for the given identical fault symptoms because of the degradation and aging. This paper suggests a new fault diagnosis approach using dynamic Bayesian networks (DBNs). It aims at identifying the component faults and distinguishing the fault types, i.e., TF, IF, and permanent faults. We construct the DBN structure and parameter models and define two judgment rules to determine the fault diagnosis results. The future scope of work can be directed toward the development of a practice fault diagnosis system for an electronic system to validate the proposed methodology.