{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T21:07:21Z","timestamp":1761599241531,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"],"award-info":[{"award-number":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"]}]},{"name":"Fundamental Research Program of Yunnan Province","award":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"],"award-info":[{"award-number":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"]}]},{"name":"PhD research startup foundation of Yunnan Normal University","award":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"],"award-info":[{"award-number":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"]}]},{"name":"Project of Educational Commission of Yunnan Province of China","award":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"],"award-info":[{"award-number":["No. 51765022","No. 41971392","No. 202201AU 070055","No. 01000205020503131","2022J0131"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA\u2013SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority.<\/jats:p>","DOI":"10.3390\/e24111696","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T03:07:23Z","timestamp":1669000043000},"page":"1696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5847-0601","authenticated-orcid":false,"given":"Mingxiu","family":"Yi","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengjiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limiao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jintao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhua","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuyi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","article-title":"A review on data-driven fault severity assessment in rolling bearings","volume":"99","author":"Cerrada","year":"2018","journal-title":"Mech. 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