A new paper is published by SDA-Lab at the journal of “Sensors” (MDPI) (imf: 3.847 Q1). The paper titled “A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train” proposed a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. This paper is authored by Dr. Abdollah Malekjafarian, Dr. Muhammad Arslan Khan, Chalres-Antoine Sarrabezolles and Dr. Fatemeh Golpayegani.
In this paper, an Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.
The paper is published fully open access and can be found in the following link: