{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:24:10Z","timestamp":1753874650877,"version":"3.41.2"},"reference-count":31,"publisher":"AIP Publishing","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51965034","51565028"],"award-info":[{"award-number":["51965034","51565028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:p>A new fault diagnosis approach based on bearing current signals is proposed in this paper. First, in view of strong background noise of the current signal, the variational modal decomposition method is applied to decompose the bearing current signal to obtain multiple intrinsic mode functions, and then the intrinsic mode functions are constructed as the input feature vector according to the kurtosis. Second, to avoid the influence of random forest parameters on the random forest classifier, a random forest faulty bearing diagnostic model optimized by the whale algorithm is established. Finally, the accuracy rate and confusion matrix are adopted to evaluate the prediction effects of both established and traditional models. The classification accuracy of the real damaged bearing fault type can reach 95.11%. The fault diagnosis accuracy of manually damaged bearings can reach 93.83%. The results show that the method proposed in this paper has high accuracy and good generalization ability for bearing fault diagnosis.<\/jats:p>","DOI":"10.1063\/5.0187651","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T13:36:24Z","timestamp":1709040984000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":1,"title":["A novel bearing current signal diagnosis method combining variational modal decomposition and improved random forests"],"prefix":"10.1063","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2497-6537","authenticated-orcid":false,"given":"Heyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology , Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9211-0335","authenticated-orcid":false,"given":"Yuqiao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology , Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1749-956X","authenticated-orcid":false,"given":"Jieshan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology , Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"2024022713361902700_c1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/lubricants11010014","article-title":"A review of research on wind turbine bearings\u2019 failure analysis and fault diagnosis","volume":"11","year":"2022","journal-title":"Lubricants"},{"key":"2024022713361902700_c2","doi-asserted-by":"publisher","first-page":"8330","DOI":"10.3390\/s22218330","article-title":"A review on rolling bearing fault signal detection methods based on different sensors","volume":"22","year":"2022","journal-title":"Sensors"},{"key":"2024022713361902700_c3","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.13334\/j.0258-8013.pcsee.210760","article-title":"Summary of rolling bearing fault diagnosis methods for signal characteristic analysis of AC motors","volume":"42","year":"2022","journal-title":"Chin. 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