{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:46:33Z","timestamp":1775324793310,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"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":["2022YFD2001805"],"award-info":[{"award-number":["2022YFD2001805"]}]},{"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":["2022YFD2001805"],"award-info":[{"award-number":["2022YFD2001805"]}]},{"name":"National key research and development program","award":["BZ2022002"],"award-info":[{"award-number":["BZ2022002"]}]},{"name":"National key research and development program","award":["CX(22)3101"],"award-info":[{"award-number":["CX(22)3101"]}]},{"name":"National key research and development program","award":["2022YFD2001805"],"award-info":[{"award-number":["2022YFD2001805"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.<\/jats:p>","DOI":"10.3390\/s23115137","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"23","author":[{"given":"Xinmin","family":"Song","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-4707","authenticated-orcid":false,"given":"Weihua","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Junbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Guojun","family":"Ji","sequence":"additional","affiliation":[{"name":"Essen Agricultural Machinery Changzhou Co., Ltd., Changzhou 213000, China"}]},{"given":"Ghulam","family":"Hussain","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi 23460, Pakistan"}]},{"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 210031, China"}]},{"given":"Guosheng","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"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":"112429","DOI":"10.1016\/j.chaos.2022.112429","article-title":"Adaptive stochastic resonance based convolutional neural network for image classification","volume":"162","author":"Duan","year":"2022","journal-title":"Chaos Solitons Fractals"},{"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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