{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:34:20Z","timestamp":1777898060205,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,24]],"date-time":"2024-03-24T00:00:00Z","timestamp":1711238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["2023JBZY039"],"award-info":[{"award-number":["2023JBZY039"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["L211010"],"award-info":[{"award-number":["L211010"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["3212032"],"award-info":[{"award-number":["3212032"]}]},{"name":"Beijing Natural Science Foundation","award":["2023JBZY039"],"award-info":[{"award-number":["2023JBZY039"]}]},{"name":"Beijing Natural Science Foundation","award":["L211010"],"award-info":[{"award-number":["L211010"]}]},{"name":"Beijing Natural Science Foundation","award":["3212032"],"award-info":[{"award-number":["3212032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep transfer learning has been widely used to improve the versatility of models. In the problem of cross-domain fault diagnosis in rolling bearings, most models require that the given data have a similar distribution, which limits the diagnostic effect and generalization of the model. This paper proposes a deep reconstruction transfer convolutional neural network (DRTCNN), which satisfies the domain adaptability of the model under cross-domain conditions. Firstly, the model uses a deep reconstruction convolutional automatic encoder for feature extraction and data reconstruction. Through sharing parameters and unsupervised training, the structural information of target domain samples is effectively used to extract domain-invariant features. Secondly, a new subdomain alignment loss function is introduced to align the subdomain distribution of the source domain and the target domain, which can improve the classification accuracy by reducing the intra-class distance and increasing the inter-class distance. In addition, a label smoothing algorithm considering the credibility of the sample is introduced to train the model classifier to avoid the impact of wrong labels on the training process. Three datasets are used to verify the versatility of the model, and the results show that the model has a high accuracy and stability.<\/jats:p>","DOI":"10.3390\/s24072079","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:32:36Z","timestamp":1711369956000},"page":"2079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Reconstruction Transfer Convolutional Neural Network for Rolling Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Ziwei","family":"Feng","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-8706","authenticated-orcid":false,"given":"Qingbin","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Xuedong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Feiyu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Xin","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Jianjun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Jingyi","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108569","DOI":"10.1016\/j.measurement.2020.108569","article-title":"Synchrosqueezing extracting transform and its application in bearing fault diagnosis under non-stationary conditions","volume":"173","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. 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