A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems

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

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	A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems
	Jia, Z.; Liu, Z.B.; Gan, Y.F.; Vong, C. M.
	2021-10-01
Source Publication	IEEE Transactions on Industrial Electronics (SCI-E)
ISSN	0278-0046
Pages	10087-10096
Abstract	Electronics-rich analog systems are difficult to diagnose owing to their complex working mechanisms and the variability of the working environment. In recent years, deep learning has been gradually applied to the field of circuit system fault diagnosis because of its strong ability to mine the intrinsic characteristics of signals. However, the traditional deep learning method requires a lot of effort to achieve satisfactory results due to large number of parameters, complex models, slow training speed, and large datasets. The key factors for the success of traditional deep learning methods are layer-by-layer processing, feature transformation within the model, and sufficient model complexity. Deep forest (DF) is a new feature learning model that inherits the three characteristics of the traditional deep learning model but is that it is not based on neural network. It has fewer hyperparameters, a simpler model, faster training speed. In this article, an improved DF algorithm based on nonparametric predictive inference (NPI) is proposed, named NPIDF, which can better deal with small sample data. In two typical analog filter circuit fault diagnosis experiments, it is proved that DF and NPIDF achieve good diagnosis effect, and NPIDF performance is better, showing a greater advantage in small sample data.
Keyword	Analog circuits deep forest (DF) diagnosis failure fault
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	58833
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Liu, Z.B.
Recommended Citation GB/T 7714	Jia, Z.,Liu, Z.B.,Gan, Y.F.,et al. A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems[J]. IEEE Transactions on Industrial Electronics (SCI-E), 2021, 10087-10096.
APA	Jia, Z.., Liu, Z.B.., Gan, Y.F.., & Vong, C. M. (2021). A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems. IEEE Transactions on Industrial Electronics (SCI-E), 10087-10096.
MLA	Jia, Z.,et al."A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems".IEEE Transactions on Industrial Electronics (SCI-E) (2021):10087-10096.

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