Electronic System Hierarchical Fault Diagnosis Method

As the scale of electronic systems continues to expand and their structures become increasingly complex, the probability of faults occurring and the difficulty of diagnosis have significantly increased. In response to the hierarchical, propagative, correlative, and uncertain characteristics of faults in electronic systems, this paper proposes a hierarchical fault diagnosis model that constructs a diagnosis system at three levels: module level, circuit level, and component level. The module-level diagnosis combines fuzzy fault trees with Bayesian networks, using key importance, fuzzy importance, and posterior probabilities to achieve fault localization; the circuit-level diagnosis is based on cross-correlation analysis, comparing the correlation between input and output signals with standard signals to make status judgments; while the component-level diagnosis employs a Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) model to accurately identify multiple fault modes. Verification through examples of a radar transmitter and typical circuits indicates that this method can significantly improve the efficiency and accuracy of fault diagnosis, demonstrating good engineering application value.