A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Abstract

In this paper, 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. 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 energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. 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.

Railway Cloud: Management and Orchestration Functionality Designed as Microservices

“…Most of the existing research focused on railway clouds is devoted to specific cloudbased railways applications [37]. These applications cover automatic train supervision [38], virtual coupling [39], track surveillance and monitoring [40,41], fault detection in railway infrastructure components [42], predictive maintenance [43], smart ticketing systems [44,45], train movements prediction [46], operation and maintenance of rail power supply systems [47], and security-related issues [48,49].…”

Identification of optimal accelerometer placement on trains for railway switch wear monitoring via multibody simulation

“…The collected data is significantly constrained by specific installation positions, posing difficulties in supporting a comprehensive analysis of tracks in the area of interest for decision-makers. A current trend in railway health monitoring to overcome this limitation is to install accelerometers on trains (Chudzikiewicz et al, 2014;Fernández-Bobadilla and Martin, 2023;Malekjafarian et al, 2023) utilized accelerometers on wheelsets to compute a defined track quality indicator, while (Malekjafarian et al, 2021) successfully differentiated between healthy and damaged tracks using accelerometers in the bogie on a service train. They assumed that accelerometers installed on wheelsets or in the bogie were the most important, considering the purpose of the track quality indicator (close to the track), the existing installation, and their lesser susceptibility to the suspension system.…”

Identification of Key Factors Influencing Sound Insulation Performance of High-Speed Train Composite Floor Based on Machine Learning

“…Aloisio et al [18] built classification models based on multinomial logistic regression (MLR) and artificial neural networks (ANNs); they calibrated seismic data that were theoretically less affected by personal bias. Malekjafarian et al [19] proposed an artificial neural network (ANN) algorithm to deal with the acceleration response energy of trains, using the acceleration response measured on trains in service to detect the stiffness loss of track sublayers.…”