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

Le-Rui Chen; Umer Sadiq Khan; Muhammad Kashif Khattak; Sheng-Jun Wen; Hai-Quan Wang; He-Yu Hu

doi:10.1063/5.0142657

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

Rev Sci Instrum. 2023 May 1;94(5):054709. doi: 10.1063/5.0142657.

Authors

Le-Rui Chen¹, Umer Sadiq Khan², Muhammad Kashif Khattak³, Sheng-Jun Wen¹, Hai-Quan Wang¹, He-Yu Hu⁴

Affiliations

¹ College of Aviation, Zhongyuan University of Technology, Zhengzhou 451191, China.
² School of Computer and Information Science and Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, Hubei 432000, China.
³ Department of Computer Science and Information Technology, University of Poonch Rawalakot, Azad Kashmir 12350, Pakistan.
⁴ State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

PMID: 37222579
DOI: 10.1063/5.0142657

Abstract

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.