Application of DBN and GWO-SVM in analog circuit fault diagnosis

Abstract For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method bas...

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Autores principales:	Xiyuan Su, Changqing Cao, Xiaodong Zeng, Zhejun Feng, Jingshi Shen, Xu Yan, Zengyan Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e79eb9c81fb4c2db031d314a3bc1c38
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spelling	oai:doaj.org-article:4e79eb9c81fb4c2db031d314a3bc1c382021-12-02T14:26:20ZApplication of DBN and GWO-SVM in analog circuit fault diagnosis10.1038/s41598-021-86916-62045-2322https://doaj.org/article/4e79eb9c81fb4c2db031d314a3bc1c382021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86916-6https://doaj.org/toc/2045-2322Abstract For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.Xiyuan SuChangqing CaoXiaodong ZengZhejun FengJingshi ShenXu YanZengyan WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiyuan Su Changqing Cao Xiaodong Zeng Zhejun Feng Jingshi Shen Xu Yan Zengyan Wu Application of DBN and GWO-SVM in analog circuit fault diagnosis
description	Abstract For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.
format	article
author	Xiyuan Su Changqing Cao Xiaodong Zeng Zhejun Feng Jingshi Shen Xu Yan Zengyan Wu
author_facet	Xiyuan Su Changqing Cao Xiaodong Zeng Zhejun Feng Jingshi Shen Xu Yan Zengyan Wu
author_sort	Xiyuan Su
title	Application of DBN and GWO-SVM in analog circuit fault diagnosis
title_short	Application of DBN and GWO-SVM in analog circuit fault diagnosis
title_full	Application of DBN and GWO-SVM in analog circuit fault diagnosis
title_fullStr	Application of DBN and GWO-SVM in analog circuit fault diagnosis
title_full_unstemmed	Application of DBN and GWO-SVM in analog circuit fault diagnosis
title_sort	application of dbn and gwo-svm in analog circuit fault diagnosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e79eb9c81fb4c2db031d314a3bc1c38
work_keys_str_mv	AT xiyuansu applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT changqingcao applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT xiaodongzeng applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT zhejunfeng applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT jingshishen applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT xuyan applicationofdbnandgwosvminanalogcircuitfaultdiagnosis AT zengyanwu applicationofdbnandgwosvminanalogcircuitfaultdiagnosis
_version_	1718391367192805376

Application of DBN and GWO-SVM in analog circuit fault diagnosis

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