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In order to solve the above problem, this paper uses a method of combining transfer learning with deep learning. First, the deep shrinkage residual network is constructed by adding soft thresholds to extract the characteristics of bearing vibration data under noise redundancy. Then, the joint maximum mean deviation (JMMD) criterion and conditional domain adversarial (CDA) learning domain adapting network are used to align the source and target domains. At the same time, adding transferable semantic augmentation (TSA) regular items improves alignment performance between classes. Finally, the proposed model is verified by three experiments: variable load, variable speed, and variable noise, which overcomes the shortcomings of traditional deep learning and shallow transfer learning algorithms.<\/jats:p>","DOI":"10.1155\/2021\/5714240","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T01:05:12Z","timestamp":1638839112000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Xinyu","family":"Yang","sequence":"first","affiliation":[]},{"given":"Fulin","family":"Chi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3180-2180","authenticated-orcid":false,"given":"Siyu","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.06.004"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2417501"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2021.3050382"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2013.2243743"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2972859"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108392"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/1077546316688991"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.07.028"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2984968"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939876"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.03.017"},{"key":"e_1_2_7_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.04.010"},{"key":"e_1_2_7_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04097-w"},{"key":"e_1_2_7_17_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"e_1_2_7_18_2","unstructured":"GrettonA. 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