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《China Mechanical Engineering》 2016-24
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Bearing Fault Diagnosis Based on multi-EMD,cApEn and GG Clustering Algorithm

Zhang Shuqing;Li Wei;Zhang Liguo;Hu Yongtao;Qian Lei;Jiang Wanlu;Measurement Technology and Instrumentation Key Lab of Hebei Province,Yanshan University;Automatic Research Institute of Hebei Province;  
A new method for rolling bearing fault diagnosis was introduced based on the multiEMD,cApEn and GG clustering algorithm.The rolling bearing vibration signals were decomposed first by multi-EMD to obtain several intrinsic mode function(IMF)components and a tendency item.Then the first seven IMF components involving the primary feature informations were chosen by the criteria of correlation with the original signals,and the cApEn entropies of each IMF component were composed eigenvectors.Finally,the constructed eigenvectors were put into GG classifier to recognize different fault types.The four kinds of operating states of the machine were presented by means of clustering three-dimensional graph,which instates that the unproportional sampling may be solved by the multi-EMD method and the cluster aliasing of EMD can be further solved.
【Fund】: 国家自然科学基金资助项目(51475405 61077071);; 河北省自然科学基金资助项目(F2015203413 F2015203392);; 河北省高等学校科学技术研究重点资助项目(ZD2014100);; 秦皇岛市科技计划资助项目(201502A043)
【CateGory Index】: TH133.3
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