{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:39:51Z","timestamp":1777696791412,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.<\/jats:p>","DOI":"10.3233\/ida-230994","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T10:39:42Z","timestamp":1729852782000},"page":"999-1020","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Fault identification of rolling bearing based on improved salp swarm algorithm"],"prefix":"10.1177","volume":"29","author":[{"given":"Hongwei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangrui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexi","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiaomi Technology (Wuhan) Co., Ltd, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2021.11.024"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102040"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2022.02.049"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acad1f"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.110089"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2021.11.024"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/10589759.2022.2151598"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/mi14050970"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3269594"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122108"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.104079"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109523"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107933"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/978-3-031-17483-4_11","article-title":"Variable importance and feature selection","author":"Dinov I.D.","year":"2023","unstructured":"Dinov I.D., Variable importance and feature selection, in: Data Science and Predictive Analytics: Biomedical and Health applications using R, Springer, 2023, pp.\u00a0579\u2013639.","journal-title":"Data Science and Predictive Analytics: Biomedical and Health applications using R"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-05757-7"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/2050-7038.12167"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/16878140211072990"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2023.109439"},{"key":"e_1_3_1_20_2","article-title":"Implementation of Vibrations Faults Monitoring and Detection on Gas Turbine System Based on the Support Vector Machine Approach","author":"nadji hadroug N.","year":"2023","unstructured":"nadji hadroug N., Iratni A., Hafaifa A., Bachir A., Colak I., Implementation of Vibrations Faults Monitoring and Detection on Gas Turbine System Based on the Support Vector Machine Approach, International Journal of Engineering Technologies IJET (2023).","journal-title":"International Journal of Engineering Technologies IJET"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18103510"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110475"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2006.11.003"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.04.021"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.02.002"},{"key":"e_1_3_1_27_2","unstructured":"Grandini M. 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