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《China Mechanical Engineering》 2014-21
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Rotating Machinery Fault Diagnosis Based on Local Mean Decomposition and Hidden Markov Model

Meng Zong;Yan Xiaoli;Wang Yachao;Key Laboratory of Measurement Technology and Instrumentation of Hebei Province,Yanshan University;  
Based on LMD and HMM,a new method for rotating machinery fault diagnosis was proposed.The method was applied to rolling bearing fault diagnosis.First of all,fault signals were decomposed by LMD,the instantaneous energy distribution of each signal was 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 was output by HMM classifier was in the fault state.A practical fault signal of a rolling bearing with corrosive pitting was applied to test the method.Experimental results show that the method of LMD-HMM is superior to the method of empirical mode decomposition(EMD)-HMM and can identify the rolling bearing faults accurately and effectively.
【Fund】: 国家自然科学基金资助项目(51105323)
【CateGory Index】: TH165.3
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