Temporal convolutional approach with residual multi-head attention mechanism for remaining useful life of manufacturing tools

Guo, B; Qiao, Z; Dong, H; Wang, Z; Huang, S; Xu, Z; Wu, F; Huang, C; Ni, Q

Temporal convolutional approach with residual multi-head attention mechanism for remaining useful life of manufacturing tools

Guo, B Qiao, Z Dong, H Wang, Z Huang, S Xu, Z Wu, F Huang, C Ni, Q

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2024, 128
Issue Date:: 2024-02-01

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Field	Value	Language
dc.contributor.author	Guo, B
dc.contributor.author	Qiao, Z
dc.contributor.author	Dong, H
dc.contributor.author	Wang, Z
dc.contributor.author	Huang, S
dc.contributor.author	Xu, Z
dc.contributor.author	Wu, F
dc.contributor.author	Huang, C
dc.contributor.author	Ni, Q https://orcid.org/0000-0001-7537-3934
dc.date.accessioned	2025-01-07T00:03:40Z
dc.date.available	2025-01-07T00:03:40Z
dc.date.issued	2024-02-01
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2024, 128
dc.identifier.issn	0952-1976
dc.identifier.issn	1873-6769
dc.identifier.uri	http://hdl.handle.net/10453/183051
dc.description.abstract	Predicting the remaining useful life of manufacturing tools based on condition maintenance is a critical task in prognostics and health management systems. Existing methods have limitations in modeling variable-length sequence data and extracting sufficient local and global dependencies. To address these issues, a temporal convolutional approach with residual multi-head attention mechanism is proposed. The temporal convolution efficiently models variable-length sequence data and extracts long-term dependency features in the time dimension. The residual multi-head attention module captures local and global dependency features in the sequence features and extracts the tool key degradation features, which further improves the performance of the model. To validate the predictive performance of the temporal convolutional approach with residual multi-head attention mechanism, two manufacturing tool datasets are used in the remaining useful life experiments. The results demonstrate the effectiveness and superiority of the proposed method compared to other advanced methods.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Engineering Applications of Artificial Intelligence
dc.relation.isbasedon	10.1016/j.engappai.2023.107538
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Temporal convolutional approach with residual multi-head attention mechanism for remaining useful life of manufacturing tools
dc.type	Journal Article
utslib.citation.volume	128
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-07T00:03:38Z
pubs.publication-status	Published
pubs.volume	128

Abstract:

Predicting the remaining useful life of manufacturing tools based on condition maintenance is a critical task in prognostics and health management systems. Existing methods have limitations in modeling variable-length sequence data and extracting sufficient local and global dependencies. To address these issues, a temporal convolutional approach with residual multi-head attention mechanism is proposed. The temporal convolution efficiently models variable-length sequence data and extracts long-term dependency features in the time dimension. The residual multi-head attention module captures local and global dependency features in the sequence features and extracts the tool key degradation features, which further improves the performance of the model. To validate the predictive performance of the temporal convolutional approach with residual multi-head attention mechanism, two manufacturing tool datasets are used in the remaining useful life experiments. The results demonstrate the effectiveness and superiority of the proposed method compared to other advanced methods.

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http://hdl.handle.net/10453/183051