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

Amer Shebani, Simon Iwnicki

Research output: Contribution to journalArticle

10 Citations (Scopus)

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 disc 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.

Original languageEnglish
Pages (from-to)173-184
Number of pages12
JournalWear
Volume406-407
Early online date31 Jan 2018
DOIs
Publication statusPublished - 15 Jul 2018

Fingerprint

rails
wheels
Rails
Wheels
Wear of materials
Neural networks
predictions
profilometers
replicas
profiles
Derailments
yaw
comfort
field tests
economy
engineers
maintenance
planning

Cite this

@article{e13a06077f2f4e63aeea8df80e607f27,
title = "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 disc 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.",
keywords = "Wheel wear, Rail wear, Replica material, Alicona profilometer, Wheel/rail wear prediction, Neural network",
author = "Amer Shebani and Simon Iwnicki",
year = "2018",
month = "7",
day = "15",
doi = "10.1016/j.wear.2018.01.007",
language = "English",
volume = "406-407",
pages = "173--184",
journal = "Wear",
issn = "0043-1648",
publisher = "Elsevier BV",

}

Prediction of wheel and rail wear under different contact conditions using artificial neural networks. / Shebani, Amer; Iwnicki, Simon.

In: Wear, Vol. 406-407, 15.07.2018, p. 173-184.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of wheel and rail wear under different contact conditions using artificial neural networks

AU - Shebani, Amer

AU - Iwnicki, Simon

PY - 2018/7/15

Y1 - 2018/7/15

N2 - 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 disc 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.

AB - 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 disc 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.

KW - Wheel wear

KW - Rail wear

KW - Replica material

KW - Alicona profilometer

KW - Wheel/rail wear prediction

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=85046379272&partnerID=8YFLogxK

U2 - 10.1016/j.wear.2018.01.007

DO - 10.1016/j.wear.2018.01.007

M3 - Article

VL - 406-407

SP - 173

EP - 184

JO - Wear

JF - Wear

SN - 0043-1648

ER -