Integrating causal representations with domain adaptation for fault diagnosis
In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has att...
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| Published in: | Reliability engineering & system safety Vol. 260; p. 110999 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.08.2025
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| ISSN: | 0951-8320 |
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| Abstract | In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has attracted increasing attention. However, most CDFD methods rely on statistical dependencies, which restricts their ability to uncover intrinsic mechanisms and affects both performance and reliability. In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning with a graph autoencoder to learn invariant representations across domains, thereby improving the robustness of the prediction model. It further employs domain adversarial networks to align feature distributions, thus mitigating conditional distribution disparities between source domain data and target fault data, ultimately enhancing model performance. Experimental results on various bearing fault datasets demonstrate that the proposed cross-domain fault diagnosis model can effectively utilize related source domain data to guide fault classification tasks in the target domain and achieve more robust fault predictions.
•We propose a new cross-domain fault diagnosis approach based on causal representation learning.•It can extract invariant features across domains while reducing the influence of irrelevant ones.•Experimental results on various datasets demonstrate the effectiveness of the proposed method. |
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| AbstractList | In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has attracted increasing attention. However, most CDFD methods rely on statistical dependencies, which restricts their ability to uncover intrinsic mechanisms and affects both performance and reliability. In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning with a graph autoencoder to learn invariant representations across domains, thereby improving the robustness of the prediction model. It further employs domain adversarial networks to align feature distributions, thus mitigating conditional distribution disparities between source domain data and target fault data, ultimately enhancing model performance. Experimental results on various bearing fault datasets demonstrate that the proposed cross-domain fault diagnosis model can effectively utilize related source domain data to guide fault classification tasks in the target domain and achieve more robust fault predictions.
•We propose a new cross-domain fault diagnosis approach based on causal representation learning.•It can extract invariant features across domains while reducing the influence of irrelevant ones.•Experimental results on various datasets demonstrate the effectiveness of the proposed method. |
| ArticleNumber | 110999 |
| Author | Jiang, Ming Zhang, Fode Gao, Jiahui Zhou, Kuang |
| Author_xml | – sequence: 1 givenname: Ming surname: Jiang fullname: Jiang, Ming email: xming@mail.nwpu.edu.cn organization: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, Shaanxi, China – sequence: 2 givenname: Kuang orcidid: 0000-0002-7278-3652 surname: Zhou fullname: Zhou, Kuang email: kzhoumath@nwpu.edu.cn organization: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, Shaanxi, China – sequence: 3 givenname: Jiahui surname: Gao fullname: Gao, Jiahui email: gaojiahui_0425@mail.nwpu.edu.cn organization: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, Shaanxi, China – sequence: 4 givenname: Fode orcidid: 0000-0002-4733-9752 surname: Zhang fullname: Zhang, Fode email: fredzh@swufe.edu.cn organization: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, PR China |
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| Keywords | Fault diagnosis Causal representations Invariant representations Transfer learning |
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