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A single domain generalization fault diagnosis method based on multi-scale style enhancement and causal contribution alignment.

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Title: A single domain generalization fault diagnosis method based on multi-scale style enhancement and causal contribution alignment.
Authors: Ding, Jiaman1,2 (AUTHOR), Luo, Jiachen1,2,3 (AUTHOR), Jia, Lianyin1,2 (AUTHOR) lianyinjia@kust.edu.cn, Wang, Hongbin1,2 (AUTHOR), Fu, Xiaodong1,2 (AUTHOR)
Source: Engineering Applications of Artificial Intelligence. Aug2026:Part 2, Vol. 178, pN.PAG-N.PAG. 1p.
Subjects: Fault diagnosis, Causal inference, Machine learning, Contrastive learning
Abstract: In recent years, single-domain generalization(SDG) fault diagnosis has become a prominent research focus in intelligent fault diagnosis due to its ability to generalize to previously unseen target domains based solely on a single source domain. The primary aim of domain generalization is to identify the intrinsic invariances underlying diverse data distributions, which have been found to be closely related to causality. While most existing fault diagnosis methods based on causal inference emphasize the invariance of causal features across domains, this study considers a stronger form of stability—namely, the cross domain consistency of features' causal contributions to fault labels. Accordingly, a novel fault diagnosis method is proposed, which integrates Multi-scale Style Enhancement (MSSE) with Causal Contribution Alignment (CCA) to achieve SDG. First, to make up for the lack of data diversity in the source domain, domain shifts are simulated and diverse pseudo-domain samples are generated using a MSSE module. Second, causal contributions of features to diagnostic labels are quantified through causal attribution. Finally, the alignment of causal contributions of features between source and pseudo domains is enforced through contrastive learning and domain adversarial training, thereby promoting stable and cross domain invariant causal representations. Comprehensive experimental evaluations on two benchmark datasets verify that the proposed method consistently outperforms existing fault diagnosis approaches. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194126586
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PubType: Academic Journal
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  Data: A single domain generalization fault diagnosis method based on multi-scale style enhancement and causal contribution alignment.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Jiaman%22">Ding, Jiaman</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Jiachen%22">Luo, Jiachen</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Lianyin%22">Jia, Lianyin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lianyinjia@kust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Hongbin%22">Wang, Hongbin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Xiaodong%22">Fu, Xiaodong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Aug2026:Part 2, Vol. 178, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Contrastive+learning%22">Contrastive learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In recent years, single-domain generalization(SDG) fault diagnosis has become a prominent research focus in intelligent fault diagnosis due to its ability to generalize to previously unseen target domains based solely on a single source domain. The primary aim of domain generalization is to identify the intrinsic invariances underlying diverse data distributions, which have been found to be closely related to causality. While most existing fault diagnosis methods based on causal inference emphasize the invariance of causal features across domains, this study considers a stronger form of stability—namely, the cross domain consistency of features' causal contributions to fault labels. Accordingly, a novel fault diagnosis method is proposed, which integrates Multi-scale Style Enhancement (MSSE) with Causal Contribution Alignment (CCA) to achieve SDG. First, to make up for the lack of data diversity in the source domain, domain shifts are simulated and diverse pseudo-domain samples are generated using a MSSE module. Second, causal contributions of features to diagnostic labels are quantified through causal attribution. Finally, the alignment of causal contributions of features between source and pseudo domains is enforced through contrastive learning and domain adversarial training, thereby promoting stable and cross domain invariant causal representations. Comprehensive experimental evaluations on two benchmark datasets verify that the proposed method consistently outperforms existing fault diagnosis approaches. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.engappai.2026.115106
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        Text: English
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      – SubjectFull: Machine learning
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              Text: Aug2026:Part 2
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