Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

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	Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning
	Liu, Z.B.; Jia, Z.; Vong, C. M.; Bu, S.H.; Han, J.W.; Tang, X.J.
	2017-06-01
Source Publication	IEEE Transactions on Industrial Informatics (SCI-E)
ISSN	1551-3203
Pages	1213-1226
Abstract	Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanismand 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.
Keyword	Analog circuits deep belief network deep learning diagnosis failure fault restricted Boltzmann machines.
Language	英語English
The Source to Article	PB_Publication
PUB ID	28762
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Vong, C. M.
Recommended Citation GB/T 7714	Liu, Z.B.,Jia, Z.,Vong, C. M.,et al. Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning[J]. IEEE Transactions on Industrial Informatics (SCI-E), 2017, 1213-1226.
APA	Liu, Z.B.., Jia, Z.., Vong, C. M.., Bu, S.H.., Han, J.W.., & Tang, X.J. (2017). Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning. IEEE Transactions on Industrial Informatics (SCI-E), 1213-1226.
MLA	Liu, Z.B.,et al."Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning".IEEE Transactions on Industrial Informatics (SCI-E) (2017):1213-1226.

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