A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems

Xu, Y; Ji, JC; Ni, Q; Feng, K; Beer, M; Chen, H

A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems

Xu, Y Ji, JC Ni, Q Feng, K Beer, M Chen, H

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2023, 200
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Xu, Y
dc.contributor.author	Ji, JC
dc.contributor.author	Ni, Q
dc.contributor.author	Feng, K
dc.contributor.author	Beer, M
dc.contributor.author	Chen, H
dc.date.accessioned	2023-11-07T02:10:23Z
dc.date.available	2023-11-07T02:10:23Z
dc.date.issued	2023-10-01
dc.identifier.citation	Mechanical Systems and Signal Processing, 2023, 200
dc.identifier.issn	0888-3270
dc.identifier.issn	1096-1216
dc.identifier.uri	http://hdl.handle.net/10453/173145
dc.description.abstract	Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.
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.2023.110609
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	A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
dc.type	Journal Article
utslib.citation.volume	200
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	2023-11-07T02:10:22Z
pubs.publication-status	Published
pubs.volume	200

Abstract:

Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.

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http://hdl.handle.net/10453/173145