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However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence\u2019s global characteristics while the bearing vibration signals\u2019 global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.<\/jats:p>","DOI":"10.1177\/0142331219844555","type":"journal-article","created":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T07:09:40Z","timestamp":1556694580000},"page":"4013-4022","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":25,"title":["Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1764-9346","authenticated-orcid":false,"given":"Keheng","family":"Zhu","sequence":"first","affiliation":[{"name":"Logistics Engineering School, Shanghai Maritime University, 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