Fault diagnosis of circuit system based on multi-sources information fusion

Based on genetic algorithm (GA), neural network, fuzzy theory and data fusion technology, a new fault diagnosis method for analog circuits with tolerance is proposed. The feature extraction, selection of training samples, structure of diagnosis system, GA-BP algorithm and synthetic decision method are dealt with. The proposed approach selects multiform circuit signatures to diagnose the circuits without sufficient accessible nodes and uses GA to optimize the structure and original weight distribution of BP networks. The GA-BP networks are adopted to implement the local diagnosis and each of them classes faults based on single kind of circuit signatures. Under considering the importance of output information from the GA-BP networks, the decision fusion is performed by using fuzzy integral. The realization of the proposed strategy is expounded by using a practical circuit. The experiment results show that the proposed method has the capability to diagnose multiple faults and single faults in tolerance circuits and gains satisfactory accuracy.