Joint Threshold Learning Convolutional Networks for Intelligent Fault Diagnosis Under Nonstationary Conditions

Li, S; Xu, Y; Feng, K; Wang, Y; Sun, B; Yan, X; Sheng, X; Zhang, K; Zheng, J; Ni, Q

Joint Threshold Learning Convolutional Networks for Intelligent Fault Diagnosis Under Nonstationary Conditions

Li, S Xu, Y Feng, K Wang, Y Sun, B Yan, X Sheng, X Zhang, K Zheng, J Ni, Q

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2023, 72, pp. 1-11
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Li, S
dc.contributor.author	Xu, Y
dc.contributor.author	Feng, K
dc.contributor.author	Wang, Y
dc.contributor.author	Sun, B
dc.contributor.author	Yan, X
dc.contributor.author	Sheng, X
dc.contributor.author	Zhang, K
dc.contributor.author	Zheng, J
dc.contributor.author	Ni, Q
dc.date.accessioned	2024-05-10T19:46:24Z
dc.date.available	2024-05-10T19:46:24Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2023, 72, pp. 1-11
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/178833
dc.description.abstract	Rotating machines are essential components in manufacturing, power generation, transportation, and aerospace industries. Nevertheless, most existing diagnosis methodologies are developed based on the assumption that machinery operates under stable conditions, which may limit their ability to uncover sufficient discriminative features when confronted with high noise levels. To tackle this issue, this article proposes a joint threshold learning convolutional network (JT-LCN) for rotating machinery diagnosis under nonstationary conditions. The major contributions of this research work can be summarized and highlighted as follows: 1) proposing a novel plug-and-play joint-thresholding module (JTM) that uses both soft and hard threshold mechanisms for intelligent signal denoising; 2) constructing an end-to-end network architecture, termed JT-LCN, which achieves a lightweight design through the use of depthwise separable convolution (DSC) and effectively purifies and identifies discriminative fault-related information by progressively using the JTM; and 3) introducing a dynamic self-knowledge distillation approach to enhance the generalization ability of the proposed network while minimizing computational costs and run-time memory requirements. Comprehensive experimental results conclusively demonstrate that the developed JT-LCN outperforms competition state-of-the-art approaches.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2023.3301863
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0299 Other Physical Sciences, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Joint Threshold Learning Convolutional Networks for Intelligent Fault Diagnosis Under Nonstationary Conditions
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	0299 Other Physical Sciences
utslib.for	0906 Electrical and Electronic 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	in_progress	*
dc.date.updated	2024-05-10T19:46:18Z
pubs.publication-status	Published
pubs.volume	72

Abstract:

Rotating machines are essential components in manufacturing, power generation, transportation, and aerospace industries. Nevertheless, most existing diagnosis methodologies are developed based on the assumption that machinery operates under stable conditions, which may limit their ability to uncover sufficient discriminative features when confronted with high noise levels. To tackle this issue, this article proposes a joint threshold learning convolutional network (JT-LCN) for rotating machinery diagnosis under nonstationary conditions. The major contributions of this research work can be summarized and highlighted as follows: 1) proposing a novel plug-and-play joint-thresholding module (JTM) that uses both soft and hard threshold mechanisms for intelligent signal denoising; 2) constructing an end-to-end network architecture, termed JT-LCN, which achieves a lightweight design through the use of depthwise separable convolution (DSC) and effectively purifies and identifies discriminative fault-related information by progressively using the JTM; and 3) introducing a dynamic self-knowledge distillation approach to enhance the generalization ability of the proposed network while minimizing computational costs and run-time memory requirements. Comprehensive experimental results conclusively demonstrate that the developed JT-LCN outperforms competition state-of-the-art approaches.

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http://hdl.handle.net/10453/178833