New approach for fast fault diagnosis of large-scale analogue circuits.

To solve the problems of complex fault location process and heavy workload before test using traditional large-scale circuit fault diagnosis methods in the multi-fault conditions, this paper presented a new approach for fault diagnosis using group decomposition. Low dimensional fault eigenvectors could be acquired by decomposing the large-scale analogue circuit in accordance with its topological property and group decomposition criteria. Based on ideas of pattern recognition, neural network which had highly parallel classify capability was selected as classifier and wavelet function which had fast convergence property was selected as hidden layer's transfer excitation function. The simulation results show that this method can achieve fast fault feature vector classification and fault diagnosis results. Comparing with the existing multiple-test-condition (MTC) method and intersection tearing method, this approach has smaller amount of work before test, fewer diagnostic times, less computation, better capability of diagnosing multiple faults and stronger engineering practicality. [ABSTRACT FROM AUTHOR]