{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:54:31Z","timestamp":1781016871549,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L211009"],"award-info":[{"award-number":["L211009"]}]},{"name":"Beijing Natural Science Foundation","award":["XK2020-04"],"award-info":[{"award-number":["XK2020-04"]}]},{"name":"Beijing Natural Science Foundation","award":["2021JXKFJJ01"],"award-info":[{"award-number":["2021JXKFJJ01"]}]},{"name":"Joint Project of BRC-BC","award":["L211009"],"award-info":[{"award-number":["L211009"]}]},{"name":"Joint Project of BRC-BC","award":["XK2020-04"],"award-info":[{"award-number":["XK2020-04"]}]},{"name":"Joint Project of BRC-BC","award":["2021JXKFJJ01"],"award-info":[{"award-number":["2021JXKFJJ01"]}]},{"name":"Key Laboratory Hunan Province of Health Maintenance Mechanical Equipment","award":["L211009"],"award-info":[{"award-number":["L211009"]}]},{"name":"Key Laboratory Hunan Province of Health Maintenance Mechanical Equipment","award":["XK2020-04"],"award-info":[{"award-number":["XK2020-04"]}]},{"name":"Key Laboratory Hunan Province of Health Maintenance Mechanical Equipment","award":["2021JXKFJJ01"],"award-info":[{"award-number":["2021JXKFJJ01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen\u2013Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model\u2019s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.<\/jats:p>","DOI":"10.3390\/s23042137","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T04:38:29Z","timestamp":1676349509000},"page":"2137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers"],"prefix":"10.3390","volume":"23","author":[{"given":"Huaqing","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mechanical Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Mechanical Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingwei","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Mechanical Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4297-1668","authenticated-orcid":false,"given":"Liuyang","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Health Monitoring and Self-recovery for High-end Mechanical Equipment, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109995","DOI":"10.1016\/j.ymssp.2022.109995","article-title":"Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group","volume":"188","author":"Han","year":"2023","journal-title":"Mech. 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