Fault Diagnosis Method for Rolling Bearing Based on Differential-basedEmpirical Mode Decomposition and Hidden Markov Model
MENG Zong1,2,YAN Xiao-li1
1. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao, Heibei 066004, China;
2. National Eng Research Center for Equipment & Tech of Cold Rolling Strip, Qinhuangdao, Heibei 066004, China
Abstract： Based on the differential-based empirical mode decomposition（DEMD）and hidden Markov model(HMM), a new method for rotating machinery fault diagnosis is proposed. The method is applied to rolling bearing fault diagnosis. First of all, fault signals are decomposed by DEMD, the instantaneous energy distribution of each signal is extracted to form the fault feature vectors, and then input the feature vectors into the HMM classifier for malfunction recognition, the maximum likelihood probability which is output by HMM classifier is in the fault state. Finally, different fault types are recognized. A practical fault signal of a rolling bearing with corrosive pitting is applied to test the method.Experimental result showed that the method of DEMD-HMM is superior to the method of EMD-HMM and can identify the rolling bearing fault accurately and effectively.
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2015, Vol. 36 
