Locating High-Speed Railway Turnout Based on Time-Frequency Slice and Mutual Correlation of Vibration Responses from Axle Box

Abstract

Vibrations collected from train axle boxes contain rich information of health conditions of railway turnouts. However, the turnout location in collected vibration data may vary from the actual one recorded. Hence, in order to detect the turnout location in the collected vibration data and identify the difference between the turnout location in the vibration and the actual turnout location recorded, this paper proposes a time-frequency slice and numerical model-driven mutual correlation method. Ensemble empirical mode decomposition (EEMD) is first applied to decompose the vibration signals collected from the train axle box. A time-frequency slice strategy is developed based on time-reassigned multi-synchrosqueezing transform (TMSST) to extract the impacts from joints and frog of the turnout. A vehicle turnout coupling dynamics model is then established to drive the mutual correlation to perform the turnout-induced impact extraction and the position difference identification. The proposed method is validated by on-site experimental data.

Keywords: health condition monitoring, time-frequency slice, impact extraction, mutual correlation, railway turnout