Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy
Na Qin · 2014
A novel method of feature extraction was proposed by combining ensemble empirical mode decomposition(EEMD)and five entropies based on the characteristics of vibration signal for high-speed train bogie in failure station.Firstly,vibration signal was decomposed by EEMD to avoid mode mixing effectively.Secondly,EEMD entropy feature was calculated for describing the complexity of intrinsic mode functions(IMFs).Vibration signals were obtained under four typical working conditions including normal condition,air spring fault,lateral damper fault and yaw damper fault.There were 280sample data including 60% training samples and 40% test samples.Analysis result shows that the method is good adaptivity for unselecting basis functions and decomposition levels. The recognition rate is above 95% at the running speed of 200km·h-1.Therefore,the feature extraction method is effective to analyze the vibration signal of high-speed train bogie in fault station.3tabs,11figs,15refs.