An intelligent method for rolling element bearing fault diagnosis based on time-wavelet energy spectrum sample entropy
DENG Feiyue1,TANG Guiji2
1.Department of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;
2.School of Energy,Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,China
In order to solve the problem of fault mode intelligent recognition and running state detection of rolling element bearing,a new method called time-wavelet energy spectrum sample entropy as the characteristic parameter was proposed for bearing fault intelligent diagnosis.Time-wavelet energy spectrum which contained fault information of bearing was obtained through the Hermitian wavelet continuous wavelet transform,and fault feature was quantitatively extracted by calculating the sample entropy of the energy spectrum.The time-wavelet energy spectrum sample entropies of bearings under different fault modes could be distinguished clearly,which could be treated as input characteristic vectors of a support vector machine (SVM) in order to complete the intelligent recognition of different fault modes of bearings.Next,the trend of running state of bearing was acquired through calculating the time-wavelet energy spectrum sample entropy of data from the whole life cycle test rig of bearing and arranging them chronologically.The early damage occurring in bearing could be effectively detected by judging the running state trend.Practical examples show the proposed method can be applied to the research for intelligent diagnosis of rolling element bearing efficiently.
邓飞跃1, 唐贵基2. 基于时间-小波能量谱样本熵的滚动轴承智能诊断方法[J]. 振动与冲击, 2017, 36(9): 28-34.
DENG Feiyue1,TANG Guiji2. An intelligent method for rolling element bearing fault diagnosis based on time-wavelet energy spectrum sample entropy. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(9): 28-34.
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