{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T18:30:47Z","timestamp":1785349847649,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52165065"],"award-info":[{"award-number":["52165065"]}]},{"name":"National Natural Science Foundation of China","award":["202102AC080002"],"award-info":[{"award-number":["202102AC080002"]}]},{"name":"Key Scientific Research Projects of Yunnan Province","award":["52165065"],"award-info":[{"award-number":["52165065"]}]},{"name":"Key Scientific Research Projects of Yunnan Province","award":["202102AC080002"],"award-info":[{"award-number":["202102AC080002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings\u2019 performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing\u2019s degradation. The stability and accuracy of the model were effectively improved.<\/jats:p>","DOI":"10.3390\/s23031110","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T01:33:51Z","timestamp":1674092031000},"page":"1110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhengjiang","family":"Feng","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China"},{"name":"Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-4933","authenticated-orcid":false,"given":"Zhihai","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China"},{"name":"Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-7308","authenticated-orcid":false,"given":"Xiaoqin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China"},{"name":"Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China"},{"name":"Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery health prognostics: A systematic review from data acquisition to RUL prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech. 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