Tamping Effectiveness Prediction Using Supervised Machine Learning Techniques
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 labeled 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 labeled with the tamping effectiveness of each track location based on the responses recorded from the IRVs before and after tamping. The labeled 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 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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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784481257
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Supplemental Notes:
- © 2018 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Tan, Chang Wei
- Webb, Geoffrey I
- Petitjean, Francois
- Reichl, Paul
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Conference:
- First International Conference on Rail Transportation 2017
- Location: Chengdu Sichuan Provence, China
- Date: 2017-7-10 to 2017-7-12
- Publication Date: 2018
Language
- English
Media Info
- Media Type: Web
- Pagination: pp 1010-1023
- Monograph Title: ICRT 2017: Railway Development, Operations, and Maintenance
Subject/Index Terms
- TRT Terms: Machine learning; Maintenance management; Railroad tracks; Tamping
- Identifier Terms: K-Nearest Neighbor Models
- Subject Areas: Maintenance and Preservation; Railroads;
Filing Info
- Accession Number: 01866270
- Record Type: Publication
- ISBN: 9780784481257
- Files: TRIS, ASCE
- Created Date: Nov 30 2022 4:37PM