Small-amplitude hunting diagnosis method for high-speed trains based on the bogie frame’s lateral–longitudinal–vertical data fusion, independent mode function reconstruction and linear local tangent space alignment

Abstract

Hunting stability is an important factor that influences the running safety of high-speed trains. Most of the existing hunting monitoring methods monitor only the standard hunting. The small-amplitude hunting, however, not only affects ride comfort but also aggravates the wheel–rail wear. Therefore, to extend the service life of wheels and rails and to improve the ride comfort, it is extremely important to monitor the small-amplitude hunting. Hunting motion is a coupled movement of lateral and yaw displacements of the wheelset. When the bogie is in an unstable state, instability will occur not only in the lateral side but also in the longitudinal and vertical sides of the bogie. To improve the robustness of the small-amplitude hunting monitoring methods, this study proposes an idea of the bogie frame’s lateral–longitudinal–vertical data fusion. In addition, the small-amplitude hunting signals have strong nonlinear characteristics, and their frequency and amplitude are unstable. Using only the amplitude or frequency to detect the small-amplitude hunting has obvious shortcomings. Therefore, a new feature extraction method based on the independent mode function reconstruction and linear local tangent space alignment (IMFR-LLTSA) is proposed. This method has been tested with three simulated signals. Finally, a method of combining the bogie frame’s lateral–longitudinal–vertical data fusion and IMFR-LLTSA is used to identify the small-amplitude hunting of high-speed trains. This method has been validated using the data of the CRH380a high-speed train running on the Shanghai–Hangzhou line, monitored by the authors’ research group. The results show that this method is superior to the single lateral diagnosis method.