A Methodology to Estimate Railway Track Conditions from Vehicle Accelerations Based on Multiple Regression

The increase of rail traffic in the last decades requires a continuous improvement of railway lines monitoring techniques, to provide higher levels of infrastructure safety and to properly manage effective maintenance plans. In this respect, the possibility to rely on in-service vehicles equipped with a simpler set of sensors (e.g., accelerometers) could increase data availability and support the maintenance strategy, that normally relies on special purpose diagnostic trains to periodically inspect the railway line. In this paper, data coming from vertical accelerometers installed on bogies of a commercial vehicle have been considered to monitor the track longitudinal level, that is the most important track geometry parameter that drives maintenance operations along high-speed lines. The proposed strategy relies on a multiple linear regression model that allows estimating the track longitudinal level, considering as input different predictors computed from the available acceleration data. The adoption of the pre-built regression model and the vehicle dynamic data allows to estimate the track geometry parameter along different sections of the line. These results can represent a useful tool to develop a methodology for track condition-based maintenance based on acceleration data from commercial vehicles