{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:40:58Z","timestamp":1783438858984,"version":"3.54.6"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Department of Education Key Laboratory","award":["17JS107"],"award-info":[{"award-number":["17JS107"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH\u2013KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH\u2013KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO\u2013VMD and WMH\u2013KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.<\/jats:p>","DOI":"10.3390\/s24155003","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T13:14:42Z","timestamp":1722604482000},"page":"5003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Rolling Bearing Fault Diagnosis Based on SABO\u2013VMD and WMH\u2013KNN"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7696-330X","authenticated-orcid":false,"given":"Guangxing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2530315","DOI":"10.1155\/2021\/2530315","article-title":"Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN","volume":"2021","author":"Lu","year":"2021","journal-title":"Math. 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