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A Novel Fault Diagnosis Method of Rolling Bearings Based on AFEWT-KDEMI

School of Electrical Engineering, Zhengzhou University, Zhengzhou 50001, China
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Entropy 2018, 20(6), 455; https://doi.org/10.3390/e20060455
Received: 17 April 2018 / Revised: 4 June 2018 / Accepted: 7 June 2018 / Published: 11 June 2018
According to the dynamic characteristics of the rolling bearing vibration signal and the distribution characteristics of its noise, a fault identification method based on the adaptive filtering empirical wavelet transform (AFEWT) and kernel density estimation mutual information (KDEMI) classifier is proposed. First, we use AFEWT to extract the feature of the rolling bearing vibration signal. The hypothesis test of the Gaussian distribution is carried out for the sub-modes that are obtained by the twice decomposition of EWT, and Gaussian noise is filtered out according to the test results. In this way, we can overcome the noise interference and avoid the mode selection problem when we extract the feature of the signal. Then we combine the advantages of kernel density estimation (KDE) and mutual information (MI) and put forward a KDEMI classifier. The mutual information of the probability density combining the unknown signal feature vector and the probability density of the known type signal is calculated. The type of the unknown signal is determined via the value of the mutual information, so as to achieve the purpose of fault identification of the rolling bearing. In order to verify the effectiveness of AFEWT in feature extraction, we extract signal features using three methods, AFEWT, EWT, and EMD, and then use the same classifier to identify fault signals. Experimental results show that the fault signal has the highest recognition rate by using AFEWT for feature extraction. At the same time, in order to verify the performance of the AFEWT-KDEMI method, we compare two classical fault signal identification methods, SVM and BP neural network, with the AFEWT-KDEMI method. Through experimental analysis, we found that the AFEWT-KDEMI method is more stable and effective. View Full-Text
Keywords: empirical wavelet transform; hypothesis test; adaptive filtering; kernel density estimation; mutual information; rolling bearings fault diagnosis empirical wavelet transform; hypothesis test; adaptive filtering; kernel density estimation; mutual information; rolling bearings fault diagnosis
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Ge, M.; Wang, J.; Zhang, F.; Bai, K.; Ren, X. A Novel Fault Diagnosis Method of Rolling Bearings Based on AFEWT-KDEMI. Entropy 2018, 20, 455.

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