Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions

Zhang, Y; Ji, JC; Ren, Z; Ni, Q; Wen, B

Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions

Zhang, Y Ji, JC Ren, Z Ni, Q Wen, B

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2023, 191
Issue Date:: 2023-05-15

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Ji, JC
dc.contributor.author	Ren, Z
dc.contributor.author	Ni, Q
dc.contributor.author	Wen, B
dc.date.accessioned	2023-11-07T02:00:58Z
dc.date.available	2023-11-07T02:00:58Z
dc.date.issued	2023-05-15
dc.identifier.citation	Mechanical Systems and Signal Processing, 2023, 191
dc.identifier.issn	0888-3270
dc.identifier.issn	1096-1216
dc.identifier.uri	http://hdl.handle.net/10453/173135
dc.description.abstract	Domain adaptation techniques have the proven ability to deal with fault diagnosis issues under variable operating conditions. They can achieve a superb diagnostic performance in single-sensor monitoring scenarios where the training and test data share the same label space. However, in practical engineering, fault modes are usually mixed with each other and new failure modes may appear during operation, which poses a challenge to the effectiveness of existing cross-domain fault diagnosis methods. Furthermore, with the increasing complexity of modern industrial systems, multi-sensor collaborative monitoring has been increasingly deployed for comprehensive measurement and detection of the complicated system. Unfortunately, there is less attention paid to multi-sensor cross-domain diagnosis in the current literature. To bridge this research gap, this paper aims to develop a novel multi-sensor open-set cross-domain fault diagnosis method. First, a convolutional neural network-based single-sensor feature extraction module and a Transformer-based multi-sensor feature fusion module are constructed for discriminative feature extraction and fusion. Second, a weighted adversarial learning scheme is built to conduct domain-invariant learning of the shared fault modes between the source and target domains. Then, a threshold-based supervised contrastive loss is defined to realize instance-level domain alignment, together with an entropy max–min loss to identify unknown class samples. The effectiveness and practicability of the proposed method are validated by a series of experiments conducted on two different types of gearboxes.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Mechanical Systems and Signal Processing
dc.relation.isbasedon	10.1016/j.ymssp.2023.110172
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Acoustics
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions
dc.type	Journal Article
utslib.citation.volume	191
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0915 Interdisciplinary 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	2023-11-07T02:00:56Z
pubs.publication-status	Published
pubs.volume	191

Abstract:

Domain adaptation techniques have the proven ability to deal with fault diagnosis issues under variable operating conditions. They can achieve a superb diagnostic performance in single-sensor monitoring scenarios where the training and test data share the same label space. However, in practical engineering, fault modes are usually mixed with each other and new failure modes may appear during operation, which poses a challenge to the effectiveness of existing cross-domain fault diagnosis methods. Furthermore, with the increasing complexity of modern industrial systems, multi-sensor collaborative monitoring has been increasingly deployed for comprehensive measurement and detection of the complicated system. Unfortunately, there is less attention paid to multi-sensor cross-domain diagnosis in the current literature. To bridge this research gap, this paper aims to develop a novel multi-sensor open-set cross-domain fault diagnosis method. First, a convolutional neural network-based single-sensor feature extraction module and a Transformer-based multi-sensor feature fusion module are constructed for discriminative feature extraction and fusion. Second, a weighted adversarial learning scheme is built to conduct domain-invariant learning of the shared fault modes between the source and target domains. Then, a threshold-based supervised contrastive loss is defined to realize instance-level domain alignment, together with an entropy max–min loss to identify unknown class samples. The effectiveness and practicability of the proposed method are validated by a series of experiments conducted on two different types of gearboxes.

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http://hdl.handle.net/10453/173135