Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains

Abstract

Hunting is an important factor in the safe operation of high-speed trains. Most techniques used for monitoring hunting aim at detecting hunting occurrence and some of them can deal with small-amplitude hunting. However, they do not provide information about evolution of small-amplitude hunting. The present work studies the evolution of small-amplitude hunting with the goal of predicting the occurrence of the hunting instability. An improved method contained multiple hidden for predicting the interval of the amplitude of small-amplitude hunting is proposed, which is improved via a two-level model. The method is computationally efficient and converges rapidly. Upon applying the proposed method to high-speed train data, the coverage probability of the resulting prediction interval is 100% and its normalized average width is 0.187, which means a higher coverage and smaller width than the interval predicted by existing methods. The confidence-level of the prediction is also high.