Analog Circuit Incipient Fault Diagnosis Using Deep Neural Network

Traditional feature extraction methods for analog circuits incipient fault diagnosis rely on signal processing technology and expert experience, the diagnostic accuracy for incipient faults is not satisfactory. To deal with these problems, a novel approach based on deep neural network is presented i...

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Bibliographic Details
Published in2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) pp. 1294 - 1302
Main Authors Guangquan, Zhao, Xu, Han, Wenyi, Teng, Xuzhou, Yang, Cong, Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
Analog circuit
Analog circuits
Autoencoders
Band-pass filters
Circuit faults
Deep Neural Network
Fault diagnosis
Feature extraction
Incipient Fault Diagnosis
Time-domain analysis
Training
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Summary:Traditional feature extraction methods for analog circuits incipient fault diagnosis rely on signal processing technology and expert experience, the diagnostic accuracy for incipient faults is not satisfactory. To deal with these problems, a novel approach based on deep neural network is presented in this paper. The proposed method includes stacked autoencoders and a softmax classifier. The stacked autoencoders extract deep features from the raw time-domain responses, and then the softmax classifies the fault mode of analog circuits automatically. The experiment results on Sallen-Key band-pass filter and Leapfrog low-pass filter circuit illustrate that the proposed method can automatically and effectively extract incipient fault features and achieve high diagnostic accuracy.
DOI:10.1109/ICEMI46757.2019.9101841