Research on fault diagnosis in analog circuit based on wavelet-neural network

Yuan Haiying; Chen Guangju; Shi Sanbao; Chen Huawei

doi:10.1109/WCICA.2006.1712845

Research on fault diagnosis in analog circuit based on wavelet-neural network

Yuan Haiying^*
, Chen Guangju
, Shi Sanbao
, Chen Huawei

^*Corresponding author for this work

University of Electronic Science and Technology of China
Wuhan University of Technology
Southwest Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

The fault diagnosis method based on wavelet-neural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	2659-2662
Number of pages	4
DOIs	https://doi.org/10.1109/WCICA.2006.1712845
State	Published - 2006
Externally published	Yes
Event	6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	1

Conference

Conference	6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/Territory	China
City	Dalian
Period	21/06/06 → 23/06/06

Keywords

Analog circuit
Fault diagnosis
Feature extraction
Wavelet-neural network

Access to Document

10.1109/WCICA.2006.1712845

Cite this

Haiying, Y., Guangju, C., Sanbao, S., & Huawei, C. (2006). Research on fault diagnosis in analog circuit based on wavelet-neural network. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 2659-2662). Article 1712845 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1). https://doi.org/10.1109/WCICA.2006.1712845

@inproceedings{71b4fc885c1f4e66acbeb71dae4405bf,

title = "Research on fault diagnosis in analog circuit based on wavelet-neural network",

abstract = "The fault diagnosis method based on wavelet-neural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method.",

keywords = "Analog circuit, Fault diagnosis, Feature extraction, Wavelet-neural network",

author = "Yuan Haiying and Chen Guangju and Shi Sanbao and Chen Huawei",

year = "2006",

doi = "10.1109/WCICA.2006.1712845",

language = "英语",

isbn = "1424403324",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "2659--2662",

booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",

}

Haiying, Y, Guangju, C, Sanbao, S & Huawei, C 2006, Research on fault diagnosis in analog circuit based on wavelet-neural network. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA)., 1712845, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 2659-2662, 6th World Congress on Intelligent Control and Automation, WCICA 2006, Dalian, China, 21/06/06. https://doi.org/10.1109/WCICA.2006.1712845

Research on fault diagnosis in analog circuit based on wavelet-neural network. / Haiying, Yuan; Guangju, Chen; Sanbao, Shi et al.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 2659-2662 1712845 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on fault diagnosis in analog circuit based on wavelet-neural network

AU - Haiying, Yuan

AU - Guangju, Chen

AU - Sanbao, Shi

AU - Huawei, Chen

PY - 2006

Y1 - 2006

N2 - The fault diagnosis method based on wavelet-neural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method.

AB - The fault diagnosis method based on wavelet-neural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method.

KW - Analog circuit

KW - Fault diagnosis

KW - Feature extraction

KW - Wavelet-neural network

UR - https://www.scopus.com/pages/publications/34047202462

U2 - 10.1109/WCICA.2006.1712845

DO - 10.1109/WCICA.2006.1712845

M3 - 会议稿件

AN - SCOPUS:34047202462

SN - 1424403324

SN - 9781424403325

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 2659

EP - 2662

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006

Y2 - 21 June 2006 through 23 June 2006

ER -

Research on fault diagnosis in analog circuit based on wavelet-neural network

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Keywords

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