Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network

Zhang, Y; Ji, J; Ma, B

Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network

Zhang, Y Ji, J

Ma, B

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2020, 156
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Ji, J https://orcid.org/0000-0003-3280-5463
dc.contributor.author	Ma, B
dc.date.accessioned	2021-01-07T00:11:31Z
dc.date.available	2021-01-07T00:11:31Z
dc.date.issued	2020-05-01
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2020, 156
dc.identifier.issn	0263-2241
dc.identifier.issn	1873-412X
dc.identifier.uri	http://hdl.handle.net/10453/145137
dc.description.abstract	© 2020 Elsevier Ltd In order to denoise the raw signal and fuse multiple sources of information for the fault diagnosis of reciprocating compressor, this paper proposes a novel convolutional deep belief network-based method and employs a novel framework fusing multi-source information to improve the performance of fault diagnosis. Firstly, signals from different sensors of the RC are input into an auto-denoising network, namely, ensemble empirical model decomposition-convolutional deep belief network, to denoise the signal and to extract more robust features by the unsupervised learning. Secondly, the extracted features of each source are input into multiple Gaussian process classifiers which are adopted as the members of probabilistic committee machine (PCM) to calculate the probabilities that each fault occurs. Finally, these probabilities are combined with an optimized weight to make a committee decision on fault type. The proposed method combines the information from multiple sources and enhances the robustness of fault diagnosis. Data from an industrial plant were collected to verify the proposed method. The obtained results demonstrate that the proposed method can effectively diagnose the RC faults with the accuracy rate of up to 91.89%. Furthermore, a comparison of the proposed method with the other methods illustrates the superiority of the proposed method for the diagnosis of RC faults.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2020.107619
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network
dc.type	Journal Article
utslib.citation.volume	156
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-07T00:11:23Z
pubs.publication-status	Published
pubs.volume	156

Abstract:

© 2020 Elsevier Ltd In order to denoise the raw signal and fuse multiple sources of information for the fault diagnosis of reciprocating compressor, this paper proposes a novel convolutional deep belief network-based method and employs a novel framework fusing multi-source information to improve the performance of fault diagnosis. Firstly, signals from different sensors of the RC are input into an auto-denoising network, namely, ensemble empirical model decomposition-convolutional deep belief network, to denoise the signal and to extract more robust features by the unsupervised learning. Secondly, the extracted features of each source are input into multiple Gaussian process classifiers which are adopted as the members of probabilistic committee machine (PCM) to calculate the probabilities that each fault occurs. Finally, these probabilities are combined with an optimized weight to make a committee decision on fault type. The proposed method combines the information from multiple sources and enhances the robustness of fault diagnosis. Data from an industrial plant were collected to verify the proposed method. The obtained results demonstrate that the proposed method can effectively diagnose the RC faults with the accuracy rate of up to 91.89%. Furthermore, a comparison of the proposed method with the other methods illustrates the superiority of the proposed method for the diagnosis of RC faults.

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http://hdl.handle.net/10453/145137