{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:03:29Z","timestamp":1776351809279,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1D1A3A03103528"],"award-info":[{"award-number":["2019R1D1A3A03103528"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"start-up grant of Queen\u2019s University Belfast","award":["D8203EEC3054789"],"award-info":[{"award-number":["D8203EEC3054789"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.<\/jats:p>","DOI":"10.3390\/s20123422","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T13:11:32Z","timestamp":1592399492000},"page":"3422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9616-6061","authenticated-orcid":false,"given":"Mien","family":"Van","sequence":"first","affiliation":[{"name":"Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3930-7283","authenticated-orcid":false,"given":"Duy Tang","family":"Hoang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9121-5442","authenticated-orcid":false,"given":"Hee Jun","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"ref_1","first-page":"169","article-title":"Review of fault detection in rolling element bearing","volume":"1","author":"Kharche","year":"2014","journal-title":"Int. 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