Analog circuit fault diagnosis based on enhanced Harris Hawks optimization algorithm with RBF neutral network

Abstract

Circuit faults are caused by the change of device parameters in the analog circuit. Aiming at the problems that the fault feature extraction is difficult and the fault signal cannot be effectively classified, an enhanced Harris Hawks algorithm is proposed to optimize the parameter optimization process in the RBF neural network, so as to realize the fault identification and diagnosis of the analog circuit. Based on wavelet packet analysis, the output response of the analog circuit is decomposed, and the fault feature vector is extracted. Taking the power conversion circuit in the electronic interlocking system as the research object, 500 sets of data are collected, and the EHHO‐RBF algorithm is trained and tested to realize the fault diagnosis of different faults, and compared with other neural network fault algorithms, the experimental results show the accuracy of fault diagnosis of EHHO‐RBF method is about 96.5%, which verifies the effectiveness and feasibility of the algorithm.