Abstract: The vibration signals of a vehicle always carry the dynamic information of the vehicle. These signals are very useful for the health monitoring and fault diagnosis. However, in many cases, because these signals have very low signal-to-noise ratio (SNR), to extract feature components becomes difficult and the applicability of information drops down. The characters of feature extraction of vibration signal were compared, among the two popular methods named wavelet analysis (WA) and Hilbert-Huang translation (HHT) and the multicorrelation of time series and empirical mode decomposition (MCTS-EMD), via simulation. And the applicability of them was analyzed using the simulation signal. The HHT and MCTS-EMD can extract the feature signal in no interference of noise or the SNR is a large number, while the WA is not suit for the feature extraction of nonlinear signal. In the strong background noise, the WA and HHT can not work well, contrasting them; the MCTS-EMD can extract the wanted object information. At last, The MCTS-EMD method was used to extract the feature signal of some special vehicle, a satisfactory result can be get, this validity of MCTSEMD was validated in the feature extraction of vehicle vibration signal.

Key words: information processing, vibration signal, feature extraction, wavelet analysis, Hilbert-Huang translation, multi-correlation of time series, empirical mode decomposition