Treatment of driving dynamic datasets of a railway vehicle aimed at condition monitoring of the track

In this paper, a proposal for the treatment of driving dynamic datasets of a railway vehicle is outlined which is aimed at the development of a track condition monitoring system that can be implemented on board of trains being operated in standard revenue service. Compared to monitoring systems installed in special diagnostic trains this concept enables a reduction in the number of sensors used, to meet stringent constraint in terms of space available for the transducers, wirings and power supply. To this aim, use is made of a Kalman-filter state estimator replacing the direct measure of vehicle dynamics at some meaningful locations in the vehicle. A performance index is defined to summarize the performance of the diagnostic unit. The proposed approach is validated using multi-body simulation.