{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:57:11Z","timestamp":1760230631667,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the online detection problem of rolling bearings, the limited amount of target bearing data leads to insufficient model in training and feature representation. It is difficult for the online detection model to construct an accurate decision boundary. To solve the problem, a multi-scale robust anomaly detection method based on data enhancement technology is proposed in this paper. Firstly, the training data are transformed into multiple subspaces through the data enhancement technology. Then, a prototype clustering method is introduced to enhance the robustness of features representation under the framework of the robust deep auto-encoding algorithm. Finally, the robust multi-scale Deep-SVDD hyper sphere model is constructed to achieve online detection of abnormal state data. Experiments are conducted on the IEEE PHM Challenge 2012 bearing data set and XJTU-TU data set. The proposed method shows much greater susceptibility to incipient faults, and it has fewer false alarms. The robust multi-scale Deep-SVDD hyper sphere model significantly improves the performance of incipient fault detection for rolling bearings.<\/jats:p>","DOI":"10.3390\/s22155681","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T04:04:00Z","timestamp":1659326640000},"page":"5681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2296-6239","authenticated-orcid":false,"given":"Linlin","family":"Kou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Mass Transit Railway Operation Corp. Ltd., Beijing 100044, China"}]},{"given":"Jiaxian","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"}]},{"given":"Yong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5335-9517","authenticated-orcid":false,"given":"Wentao","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.ymssp.2018.07.034","article-title":"A novel deep output kernel learning method for bearing fault structural diagnosis","volume":"117","author":"Mao","year":"2019","journal-title":"Mech. 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