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Multiscale dilated convolutional subdomain adaptation network with attention for unsupervised fault diagnosis of rotating machinery cross operating conditions
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
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2022 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 204, article id 112146Article in journal (Refereed) Published
Abstract [en]

Unsupervised cross-domain fault diagnosis research of rotating machinery has significant implications. However, some issues remain to be solved. For example, convolutional neural network cannot capture discriminative information in vibration signals from different scales. In addition, the extracted features should be selected to enhance informative features and suppress redundant features. Finally, global domain adaptation methods may cause different subdomains of source and target domains to be too close. To address these challenges, this paper proposes a multiscale dilated convolutional subdomain adaptation network with attention. Firstly, a multiscale dilated convolutional module is developed to extract fault features at different scales. Secondly, a squeeze-and-excitation attention mechanism is built to assign channel-level weights to these features. Finally, local maximum mean discrepancy is constructed to adapt corresponding subdomains of the two domains. The proposed method is applied to perform various unsupervised cross-domain fault diagnosis tasks, and the experimental results demonstrate its superior diagnostic performance.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 204, article id 112146
Keywords [en]
Multiscale features, Attention mechanism, Subdomain adaptation, Unsupervised cross-domain, Rotating machinery fault diagnosis
National Category
Reliability and Maintenance Computer Sciences
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-93824DOI: 10.1016/j.measurement.2022.112146ISI: 000881770500003Scopus ID: 2-s2.0-85141266906OAI: oai:DiVA.org:ltu-93824DiVA, id: diva2:1708720
Note

Validerad;2022;Nivå 2;2022-11-25 (hanlid);

Funder: National Natural Science Foundation of China (51905160); Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017)

Available from: 2022-11-05 Created: 2022-11-05 Last updated: 2023-05-08Bibliographically approved

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Lin, Janet (Jing)

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