An effective approach based on nonlinear spectrum and improved convolution neural network for analog circuit fault diagnosis

In this work, an effective approach based on a nonlinear output frequency response function (NOFRF) and improved convolution neural network is proposed for analog circuit fault diagnosis. First, the NOFRF spectra, rather than the output of the system, are adopted as the fault information of the anal...

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Bibliographic Details
Published in:Review of scientific instruments 2023-05, Vol.94 (5)
Main Authors: Chen, Le-rui, Khan, Umer Sadiq, Khattak, Muhammad Kashif, Wen, Sheng-jun, Wang, Hai-quan, Hu, He-yu
Format: Article
Language:English
Subjects:
Analog circuits
Artificial neural networks
Fault diagnosis
Frequency response functions
Scientific apparatus & instruments
Spectra
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Summary:In this work, an effective approach based on a nonlinear output frequency response function (NOFRF) and improved convolution neural network is proposed for analog circuit fault diagnosis. First, the NOFRF spectra, rather than the output of the system, are adopted as the fault information of the analog circuit. Furthermore, to further improve the accuracy and efficiency of analog circuit fault diagnosis, the batch normalization layer and the convolutional block attention module (CBAM) are introduced into the convolution neural network (CNN) to propose a CBAM-CNN, which can automatically extract the fault features from NOFRF spectra, to realize the accurate diagnosis of the analog circuit. The fault diagnosis experiments are carried out on the simulated circuit of Sallen–Key. The results demonstrate that the proposed method can not only improve the accuracy of analog circuit fault diagnosis, but also has strong anti-noise ability.
ISSN:0034-6748
1089-7623
DOI:10.1063/5.0142657