Intelligent Fault Diagnosis of a Reciprocating Compressor Using Mode Isolation Convolutional Deep Belief Networks

Zhang, Y; Ji, J

Intelligent Fault Diagnosis of a Reciprocating Compressor Using Mode Isolation Convolutional Deep Belief Networks

Zhang, Y Ji, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE/ASME Transactions on Mechatronics, 2021, 26, (3), pp. 1668-1677
Issue Date:: 2021-06-01

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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.date.accessioned	2022-05-05T20:35:29Z
dc.date.available	2022-05-05T20:35:29Z
dc.date.issued	2021-06-01
dc.identifier.citation	IEEE/ASME Transactions on Mechatronics, 2021, 26, (3), pp. 1668-1677
dc.identifier.issn	1083-4435
dc.identifier.issn	1941-014X
dc.identifier.uri	http://hdl.handle.net/10453/157068
dc.description.abstract	Due to the complex transfer paths of vibration signals, and a large number of vibration excitations, fault diagnosis of reciprocating compressors (RCs) has become one of the most challenging problems in the field of health monitoring. Focusing on fault diagnosis, a novel method, which will be referred to in this paper as mode isolation convolutional deep belief network (MI-CDBN), is proposed from the perspective of transfer path analysis, and multimodal data isolation. First, sparse filtering is applied to compress vibration signals and to reduce the computing cost. Second, the MI-CDBN is used to isolate multimodal data of different transfer paths and to calculate features using unsupervised learning. Finally, a multiclass logistic regression is employed to identify the fault types of the RC. Vibration signals from practical industries are used to validate the proposed method. The obtained results demonstrate that the proposed method has an improved performance compared to many other state-of-the-art methods widely used in the fault diagnosis of RCs.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE/ASME Transactions on Mechatronics
dc.relation.isbasedon	10.1109/TMECH.2020.3027912
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Intelligent Fault Diagnosis of a Reciprocating Compressor Using Mode Isolation Convolutional Deep Belief Networks
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0910 Manufacturing Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-05T20:35:26Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	3

Abstract:

Due to the complex transfer paths of vibration signals, and a large number of vibration excitations, fault diagnosis of reciprocating compressors (RCs) has become one of the most challenging problems in the field of health monitoring. Focusing on fault diagnosis, a novel method, which will be referred to in this paper as mode isolation convolutional deep belief network (MI-CDBN), is proposed from the perspective of transfer path analysis, and multimodal data isolation. First, sparse filtering is applied to compress vibration signals and to reduce the computing cost. Second, the MI-CDBN is used to isolate multimodal data of different transfer paths and to calculate features using unsupervised learning. Finally, a multiclass logistic regression is employed to identify the fault types of the RC. Vibration signals from practical industries are used to validate the proposed method. The obtained results demonstrate that the proposed method has an improved performance compared to many other state-of-the-art methods widely used in the fault diagnosis of RCs.

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http://hdl.handle.net/10453/157068