Wavelet entropy used in feature analysis of high speed train bogie fault signal
Na Qin, Weidong Jin, Jinsong Huang, Xiantai Gou, Peng Jiang · Jisuanji yingyong yanjiu · 2013
Performance monitoring and fault diagnosis for the critical component of bogie is very important. Simulation data of high speed train bogie fault signal was selected in the data experiment. Based on multiresolution analysis,wavelet entropies were extracted to reflect the complexity level of the vibration signal on scales. In the high dimension composed by several wavelet entropy features,the dates from four fault patterns were classified and recognition rate is above 90% when the speed over 200 km / h. The wavelet entropy feature is effective for fault signal analysis of high speed train bogie.