{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:33:35Z","timestamp":1773776015988,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T00:00:00Z","timestamp":1512432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM) classifier and back-propagation (BP) neural network classifier.<\/jats:p>","DOI":"10.3390\/e19120633","type":"journal-article","created":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T11:50:28Z","timestamp":1512474628000},"page":"633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Fault Diagnosis of Rolling Bearings Based on EWT and KDEC"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4274-1926","authenticated-orcid":false,"given":"Mingtao","family":"Ge","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4300","DOI":"10.3390\/en8054300","article-title":"Fault diagnosis and fault-tolerant control of wind turbines via a discrete time controller with a disturbance compensator","volume":"8","author":"Vidal","year":"2015","journal-title":"Energies"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1016\/j.eswa.2009.12.051","article-title":"Application of mother wavelet functions for automatic gear and bearing fault diagnosis","volume":"37","author":"Rafiee","year":"2010","journal-title":"Expert Syst. 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