{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:03:23Z","timestamp":1776823403591,"version":"3.51.2"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National University Innovation and Entrepreneurship Training Programs Foundation","award":["202210060002"],"award-info":[{"award-number":["202210060002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.<\/jats:p>","DOI":"10.3390\/s24134251","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2460-8550","authenticated-orcid":false,"given":"Ziyi","family":"Tang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China"},{"name":"Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China"}]},{"given":"Xinhao","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China"},{"name":"Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0323-0836","authenticated-orcid":false,"given":"Xinheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Maritime College, Tianjin University of Technology, Tianjin 300384, China"},{"name":"Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8111-5656","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Training Center, Tianjin University of Technology, Tianjin 300384, China"},{"name":"Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China"}]},{"given":"Jifeng","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China"},{"name":"Institute of Intelligent Control and Fault Diagnosis, Tianjin University of Technology, Tianjin 300384, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.measurement.2021.109864","article-title":"Extreme learning machine-based classifier for fault diagnosis of rotating machinery using a residual network and continuous wavelet transform","volume":"183","author":"Wei","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104919","DOI":"10.1016\/j.mechmachtheory.2022.104919","article-title":"Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines","volume":"174","author":"Jiang","year":"2022","journal-title":"Mech. 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