Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor

Abstract

Analog circuit fault diagnosis is a key problem in theory of circuit networks and has been investigated by many researchers in recent years. An approach based on sparse random projections (SRPs) and K-nearest neighbor (KNN) to the realization of analog circuit soft fault diagnosis has been presented in this paper. The proposed method uses the wavelet packet energy spectrum and sparse random projections to preprocess the time response for feature extraction. Then, the variables of the fault features are constructed, which are used to form the observation sequences of K-nearest neighbor classifier. K-nearest neighbor classifier is used to accomplish the fault diagnosis of analog circuit. In this paper, four-opamp biquad high-pass filter has been used as simulation example to verify the effectiveness of the proposed method. The simulations show that the proposed method offers higher correct fault location rate in analog circuit soft fault diagnosis application as compared with the other methods.

Analog Circuit Incipient Fault Detection Based on Attention Mechanism and Fully Convolutional Network

“…Sun et al [18] preprocessed time responses using wavelet packet energy spectra and sparse random projection, and then constructed feature variables for fault detection, forming an observation sequence for k-nearest neighbour classifiers. Luo et al [19] employed fractional wavelet transform for feature extraction and used support vector machines for fault diagnosis based on data description.…”