Forecasting Model of Hunting Instability of High-speed Train Bogie Based on Modified Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine

To forecast hunting instability state of high-speed train bogie, a new forecasting model which combines modified ensemble empirical mode decomposition (MEEMD) and least squares support vector machine (LSSVM) was presented in this paper, it focus on the normal, transition and hunting instability states of bogie vibration signal. Firstly, the vibration signal will be decomposed by MEEMD. Then, the Hilbert transformation (HT) will be used to analyze the time-frequency-energy features. Meanwhile, the energy feature of intrinsic mode functions (IMFs) will be extracted to be used to train by LSSVM. Finally, hunting instability state will be forecasted by recognizing the transition state. The results show that forecast accuracy up to 93.33%, and the accuracy and calculation time are superior to ensemble empirical mode decomposition-support vector machine (EEMD-SVM) when the train at 350 km/h. The validity and rapidity of the forecasting model were proved. © 2018, Department of Journal of the China Railway Society. All right reserved.