Prediction of wheel and rail wear under different contact conditions using artificial neural networks

Abstract

Wheel and rail wear is a significant issue in railway systems. Accurate prediction of this wear can improve economy, ride comfort, prevention of derailment and planning of maintenance interventions. Poor prediction can result in failure and consequent delay and increased costs if it is not controlled in an effective way. However, prediction of wheel and rail wear is still a great challenge for railway engineers and operators. The aim of this paper is to predict wheel wear and rail wear using an artificial neural network. Nonlinear Autoregressive models with exogenous input neural network (NARXNN) have been developed for wheel and rail wear prediction. Testing with a twin disc rig, together with measurement of wear using replica material and a profilometer have been carried out for wheel and rail wear under dry, wet and lubricated conditions and after sanding. Tests results from the twin disk rig have been used to train, validate, and test the neural network. Wheel and rail profiles plus load, speed, yaw angle, and first and second derivative of the wheel and rail profiles were used as an inputs to the neural network, while the output of neural network was the wheel wear and rail wear. Accuracy of wheel and rail wear prediction using the neural network was investigated and assessed in term of mean absolute percentage error (MAPE). The results demonstrate that the neural network can be used efficiently to predict wheel and rail wear. The methods of collecting wear data using the replica material and the profilometer have also proved effective for wheel and rail wear measurements for training and validating the neural network. The laboratory tests have aimed to validate the wear predictions for realistic wheel and rail profiles and materials but they necessarily cover only a limited set of conditions. The next steps for this work will be to test the methods for rail and wheel data from field tests.

Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

“…Step 5: The updates of biases in the hidden layer and the output layer are formulated by Equations (15) and (16), respectively:…”

“…The experimental results were trained in the ANNs program and the predicted results coincided with the experimental results. Shebani and Iwnicki [15] developed nonlinear autoregressive models with exogenous input neural network (NARXNN) for wheel and rail wear prediction.…”

Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line

“…Vertical wear appears on the upper surface of the rail head, while lateral wear occurs on the side of the rail head [3]. Rail wear depends on various parameters such as the axle load, train speed, profiles of wheel and rail, material properties of wheel and rail, track curvature, traffic type, condition of the wheel-rail contact surface, contact pressure, lubrication, and environmental effects [1,4]. Rail wear causes the location change of the contact points between wheel and rail, leading to deterioration of the wheel-rail contact geometry and instability of railway vehicles [5].…”

Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology

“…In recent years, an increasing number of tribological studies turned to the use of artificial intelligence (AI) techniques (Bucholz et al, 2012;Ali et al, 2014), including data mining (Liao et al, 2012) and artificial neural networks (Gandomi and Roke, 2015). In the last two decades, starting from the work of Jones et al (1997), the areas of successful incorporation of AI generally and neural networks (NNs) specially have been constantly expanding in tribology research and cover such diverse applications as wear of polymer composites (Kadi, 2006;Jiang et al, 2007), tool wear (Quiza et al, 2013), brake performance (Aleksendrić and Barton, 2009;Bao et al, 2012), erosion of polymers (Zhang et al, 2003), wheel and rail wear (Shebani and Iwnicki, 2018). Nevertheless, it is important to emphasize that, while AI is widely applied for diagnostics (identification), classification, and prediction (process control) (Meireles et al, 2003), much remains to be scrutinized to extend its modeling (in a narrow sense of this term) capabilities.…”