Rolling Bearing Fault Diagnosis Based on Differential-based Empirical Mode Decomposition and Fuzzy Entroy
MENG Zong1,2,JI Yan1,YAN Xiao-li1
1. Key Lab of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao, Heibei 066004, China
2. National Engineering Research Center for Eqpt & Tech of Cold Rolling Strip, Qinhuangdao, Heibei 066004, China
Abstract：A comprehensive rolling bearing fault diagnosis method combining differential-based empirical mode decomposition (DEMD) with fuzzy entropy and support vector machine(SVM) is proposed.Firstly, mechanical vibration signal is decomposed with differential-based empirical mode decomposition (DEMD) to obtain a certain number of intrinsic mode functions (IMFs) that have physical meaning. With a mutual relationship rule, the IMF components that have largest correlation coefficients with the original signal are sifted out. The fuzzy entropies of these IMFs are calculated and use as eigenvectors of fault signals, then the eigenvectors are put into SVM to identify the state of the rolling bearing. Compared with the method based on empirical mode decomposition (EMD) combined with fuzzy entropy and SVM, the experimental results show that the method of mechanical failure signals can accurately identify classification effectively.
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2016, Vol. 37 
