A Speed-Dependent Condition Monitoring System for Track Geometry Estimation Using Inertial Measurements

In this paper, a methodology to predict the track longitudinal level using bogie vertical acceleration from in-service vehicles is proposed. To account for the effect of vehicle speed, the acceleration levels are double integrated on-board the vehicle. Synthetic indicators like the RMS are then computed over predefined track sections of 100 m, to reduce the amount of data to be stored and analysed. Then, a linear regression model between the double integrated indicators and the direct track geometry measurements collected by a TRV is built, to verify the degree of correlation of the two quantities. To this end, data collected during a long-term monitoring campaign along the Italian railway network are considered. The regression model is finally adopted to predict the RMS of the longitudinal level using the signals collected on-board the vehicle. The comparison between the predicted and measured data is shown to be promising towards the possibility of condition monitoring of the track geometry both on high-speed and conventional lines.