{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:25:43Z","timestamp":1774578343848,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu International Science and Technology Cooperation Project","award":["BZ2022002"],"award-info":[{"award-number":["BZ2022002"]}]},{"name":"Jiangsu International Science and Technology Cooperation Project","award":["CX(22)3101"],"award-info":[{"award-number":["CX(22)3101"]}]},{"name":"Jiangsu International Science and Technology Cooperation Project","award":["BE2022385"],"award-info":[{"award-number":["BE2022385"]}]},{"name":"Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province","award":["BZ2022002"],"award-info":[{"award-number":["BZ2022002"]}]},{"name":"Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province","award":["CX(22)3101"],"award-info":[{"award-number":["CX(22)3101"]}]},{"name":"Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province","award":["BE2022385"],"award-info":[{"award-number":["BE2022385"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["BZ2022002"],"award-info":[{"award-number":["BZ2022002"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["CX(22)3101"],"award-info":[{"award-number":["CX(22)3101"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["BE2022385"],"award-info":[{"award-number":["BE2022385"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology.<\/jats:p>","DOI":"10.3390\/s22166281","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"6281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM"],"prefix":"10.3390","volume":"22","author":[{"given":"Junbo","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210032, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-1035","authenticated-orcid":false,"given":"Maohua","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210032, China"}]},{"given":"Yue","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210032, China"}]},{"given":"Guojun","family":"Ji","sequence":"additional","affiliation":[{"name":"Essen Agricultural Machinery Changzhou Co., Ltd., Changzhou 213000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.measurement.2018.06.047","article-title":"Weak characteristic determination for blade crack of centrifugal compressors based on underdetermined blind source separation","volume":"128","author":"He","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.isatra.2017.08.009","article-title":"An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis","volume":"71","author":"Zhang","year":"2019","journal-title":"ISA Trans."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109161","DOI":"10.1016\/j.measurement.2021.109161","article-title":"Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method","volume":"176","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107853","DOI":"10.1016\/j.ymssp.2021.107853","article-title":"Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines","volume":"160","author":"Islam","year":"2021","journal-title":"Mech. 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