Chapter
Mar 13, 2018
First International Conference on Rail Transportation 2017

Tamping Effectiveness Prediction Using Supervised Machine Learning Techniques

Publication: ICRT 2017: Railway Development, Operations, and Maintenance

ABSTRACT

Railway maintenance planning is critical in maintaining track assets. Tamping is a common railway maintenance procedure and is often used when geometrical issues are first identified. Tamping repacks ballast particles under sleepers to restore the correct geometrical position of ballasted tracks. However, historical data shows that tamping is not always effective in restoring track to a satisfactory condition. Furthermore, ineffective, or unnecessary tamping tends to reduce the lifetime of existing track. An intuitive way of preventing ineffective tamping is to predict the likely tamping effectiveness. This work aims to predict the likely tamping effectiveness ahead of time using supervised machine learning techniques. Supervised machine learning techniques predict an outcome using labelled training data. In this case, the training database consists of multivariate sensor data from instrumented revenue vehicles (IRVs). The data between the previous and current tamping dates are used. This forms a time series database labelled with the tamping effectiveness of each track location based on the responses recorded from the IRVs before and after tamping. The labelled time series database is then used to train a time series classifier for prediction. This work uses the state of the art time series classification algorithm, k-nearest neighbour (k-NN) extended to the case of multivariate time series. k-NN is a non-parametric algorithm that does not make assumptions on the underlying model of the training data. With a sufficiently large training database, non-parametric algorithms can outperform parametric algorithms. Using k-NN, the tamping effectiveness of a potential tamping location that is not in the training database, or locations in the next tamping cycle, is predicted using the expected tamping effectiveness from a location in the training database that is the most similar to the target. This allows the algorithm to effectively to identify locations where tamping is likely to be ineffective. This work achieves high accuracy in the prediction of tamping effectiveness even at 12 weeks before tamping. It is hoped that the methodology will help in assisting decision making for maintenance planning activities.

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ACKNOWLEDGEMENT

We would like to thank Mr Cameron Thompson and Mr Joshua White from the Institute of Railway Technology (IRT), Monash University for their assistance in preparing the data.

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Published In

Go to ICRT 2017
ICRT 2017: Railway Development, Operations, and Maintenance
Pages: 1010 - 1023
Editors: Wanming Zhai, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8125-7

History

Published online: Mar 13, 2018
Published in print: Mar 13, 2018

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Authors

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Chang Wei Tan [email protected]
Faculty of Information Technology, Monash Univ., Melbourne 3800, Australia (corresponding author). E-mail: [email protected]
Geoffrey I. Webb
Faculty of Information Technology, Monash Univ., Melbourne 3800, Australia
Francois Petitjean
Faculty of Information Technology, Monash Univ., Melbourne 3800, Australia
Paul Reichl
Institute of Railway Technology, Monash Univ., Melbourne 3800, Australia

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