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Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning
Liu, Zhenbao1; Jia, Zhen1; Vong, Chi-Man2; Bu, Shuhui1; Han, Junwei1; Tang, Xiaojun1
2017-06
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
Volume13Issue:3Pages:1213-1226
Abstract

Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanism and variable operational conditions. Traditionally, FDI in such systems is done through the monitoring of deviation of output signals in voltage or current at system level, which commonly arises from the degradation of one or more critical components. Therefore, FDI can be transformed to a multiclass classification task given the extracted features of the output signals in voltage or current of the circuit. Traditional feature extraction on the circuit output is mostly based on time-domain, frequency-domain, or time-frequency signal processing, which collapse high-dimensional raw signals into a lower dimensional feature set. Such low-dimensional feature set usually suffers from information loss so as to affect the accuracy of the later fault diagnosis. In order to retain as much information as possible, deep learning is proposed which employs a hierarchical structure to capture the different levels of semantic representations of the signals. In this paper, a novel fault diagnostic application of Gaussian-Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high-order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.

KeywordAnalog Circuits Deep Belief Network Deep Learning Diagnosis Failure Fault Restricted Boltzmann Machines
DOI10.1109/TII.2017.2690940
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000402929700028
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:21   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorVong, Chi-Man
Affiliation1.the Northwestern Polytechnical University, Xi’an 710072, China
2.Department of Computer and Information Science, University of Macau, Macau 999078, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Zhenbao,Jia, Zhen,Vong, Chi-Man,et al. Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2017,13(3):1213-1226.
APA Liu, Zhenbao,Jia, Zhen,Vong, Chi-Man,Bu, Shuhui,Han, Junwei,&Tang, Xiaojun.(2017).Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,13(3),1213-1226.
MLA Liu, Zhenbao,et al."Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 13.3(2017):1213-1226.
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