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Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
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Entropy 2017, 19(12), 633; https://doi.org/10.3390/e19120633
Received: 7 September 2017 / Revised: 6 November 2017 / Accepted: 20 November 2017 / Published: 5 December 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

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. View Full-Text
Keywords: rolling element bearings; empirical wavelet transform; kernel density estimation; fault diagnosis rolling element bearings; empirical wavelet transform; kernel density estimation; fault diagnosis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ge, M.; Wang, J.; Ren, X. Fault Diagnosis of Rolling Bearings Based on EWT and KDEC. Entropy 2017, 19, 633.

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