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In the recent years, the discrepancy between train and test datasets is decreased by introducing the idea of transfer learning to solve the first issue. In addition, the non-contact sensors will replace the contact sensors. In this paper, a domain adaption residual neural network (DA-ResNet) model using maximum mean discrepancy (MMD) and a residual connection is constructed for cross-domain diagnosis of roller bearings based on acoustic and vibration data. MMD is used to minimize the distribution discrepancy between the source and target domains, thereby improving the transferability of the learned features. Acoustic and vibration signals from three directions are simultaneously sampled to provide more complete bearing information. Two experimental cases are conducted to test the ideas presented. The first is to verify the necessity of multi-source data, and the second is to demonstrate that transfer operation can improve recognition accuracy in fault diagnosis.<\/jats:p>","DOI":"10.3390\/s23063068","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T04:35:41Z","timestamp":1678682141000},"page":"3068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Hang","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730000, China"}]},{"given":"Zhansi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4028-985X","authenticated-orcid":false,"given":"Jiawei","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. 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