An Investigation into the Behavior of Intelligent Fault Diagnostic Models under Imbalanced Data

Ren, Z; Ji, J; Zhu, Y; Feng, K

An Investigation into the Behavior of Intelligent Fault Diagnostic Models under Imbalanced Data

Ren, Z Ji, J

Zhu, Y Feng, K

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2024, 73, pp. 1-20
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Ren, Z
dc.contributor.author	Ji, J https://orcid.org/0000-0003-3280-5463
dc.contributor.author	Zhu, Y
dc.contributor.author	Feng, K
dc.date.accessioned	2024-08-07T04:33:15Z
dc.date.available	2024-08-07T04:33:15Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2024, 73, pp. 1-20
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/180202
dc.description.abstract	In solving the data imbalance problem, most of the existing studies ignored the effect of the number of samples on the diagnostic performance of intelligent fault diagnostic models. When the number of minority samples is small, the data imbalance situation becomes a compound problem of data imbalance and small sample, making it challenging to develop effective methods and identify core issues. This article aims to investigate the effects of the compound problem on intelligent diagnostic models by using the number of samples and the number of majority classes as indicators to assess the deterioration behavior of diagnostic models. Multiple datasets are systematically studied to explore three categories of influences: the influence of the number of minority samples on the model's learning ability, the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence speed, and the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence correctness. The exacerbation of imbalance by an increase in the number of majority classes further deepens the influence of imbalance on the convergence of the model. Furthermore, this article explores the degradation mechanism of intelligent diagnostic models. Finally, a semiquantitative deterioration process is proposed to guide future studies on imbalanced learning in experimental design and method evaluation.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2023.3343775
dc.rights	info:eu-repo/semantics/closedAccess
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	An Investigation into the Behavior of Intelligent Fault Diagnostic Models under Imbalanced Data
dc.type	Journal Article
utslib.citation.volume	73
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	closed_access	*
dc.date.updated	2024-08-07T04:33:11Z
pubs.publication-status	Published
pubs.volume	73

Abstract:

In solving the data imbalance problem, most of the existing studies ignored the effect of the number of samples on the diagnostic performance of intelligent fault diagnostic models. When the number of minority samples is small, the data imbalance situation becomes a compound problem of data imbalance and small sample, making it challenging to develop effective methods and identify core issues. This article aims to investigate the effects of the compound problem on intelligent diagnostic models by using the number of samples and the number of majority classes as indicators to assess the deterioration behavior of diagnostic models. Multiple datasets are systematically studied to explore three categories of influences: the influence of the number of minority samples on the model's learning ability, the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence speed, and the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence correctness. The exacerbation of imbalance by an increase in the number of majority classes further deepens the influence of imbalance on the convergence of the model. Furthermore, this article explores the degradation mechanism of intelligent diagnostic models. Finally, a semiquantitative deterioration process is proposed to guide future studies on imbalanced learning in experimental design and method evaluation.

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http://hdl.handle.net/10453/180202