Fault diagnosis of analogue circuits based on artificial intelligence algorithms

Abstract

Abstract The fault diagnosis problem of analog circuits has been paid more and more attention, and the realization of circuit fault feature extraction and pattern classification are two of the key problems. This paper first combines wavelet packet and energy entropy to design a fault feature extraction method based on wavelet packet entropy to solve the problem of analog circuit fault feature extraction. Secondly, the principle of SVM and particle swarm optimization algorithm are combined to design the fault diagnosis process of analog circuits based on an artificial intelligence algorithm. Finally, take the sallen-key circuit as an example to analyze the effectiveness of the wavelet packet entropy algorithm for fault feature extraction and analyze the effectiveness of this paper’s method based on the analog circuit of Wen’s bridge oscillation circuit. The results show that after wavelet packet entropy extraction of the feature vector values is less than 0.001, and the accuracy of the extraction is more than 0.98, the best parameters of the optimization is (182.4, 0.05), the false alarm rate of the fault diagnostic method is 0, the misdiagnosis rate is 0.08, the omission rate is 0, and the correctness rate of the diagnosis of the fault is 0.92. Based on this research is able to carry out the fault diagnosis of the analog circuit.