Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique

Track geometry monitoring is essential for track maintenance. Dedicated track inspection vehicles are scheduled to measure track geometry irregularities throughout the railway network, so cannot inspect each line frequently. It is desirable to inspect geometry much higher frequently using in-service trains. One possible way is estimating track geometry from vehicle–body accelerations because the accelerators can be easily installed in vehicle–body. However, inverting track geometry from vehicle–body acceleration is a pending issue. Up to now, most research has focused on vehicle dynamics modelling and simulation. In this paper, we solve the problem using deep learning method and realistic measurement data. The training and test data are the inspection data acquired by comprehensive inspection trains from three main high-speed railways in China. The proposed AM–CNN–GRU model combines an Attention Mechanism (AM), a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). The model’s inputs are vertical and lateral accelerations, and vehicle speed. The outputs are two vertical track irregularities, with wavelengths of 3–42m and 3–120 m, respectively. We evaluated the model by comparing the estimated irregularities with the actual measurements. We discussed different models, high-speed lines, sub-rail infrastructures and speed to validate the model effectiveness.