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Dealing with disruptions in railway track inspection using risk-based machine learning
Sakdirat Kaewunruen1, Mohd Haniff Osman2,3
1Department of Civil Engineering, University of Birmingham, Birmingham, B15 2TT, UK. s.kaewunruen@bham.ac.uk.
Scientific Reports
|February 7, 2023
Summary
A new data generation model uses artificial neural networks to create synthetic track measurement data. This approach enhances railway track integrity prediction and resilient operation management following disruptions.
Area of Science:
- Railway Engineering
- Data Science
- Predictive Maintenance
Background:
- Unplanned track inspections disrupt railway operations and data collection.
- Existing methods struggle to provide timely data during operational disruptions.
Purpose of the Study:
- To develop a novel data generation model for supplementary track measurements.
- To enhance the value of information for disrupted track geometry monitoring.
Main Methods:
- Proposed a nonlinear autoregressive with exogenous variables (NARX) model.
- Utilized artificial neural networks to determine model nonlinearities.
- Incorporated short-range memory dependencies and external factors.
- Managed model generalization ability using risk aversion.
Main Results:
- Generated artificial track measurement data for affected segments.
- Successfully predicted track longitudinal level deviation using degradation rate, alignment, and gauge.
- Demonstrated model reliability and accuracy on diverse datasets.
Conclusions:
- The NARX-based data generation model offers a reliable and accurate solution for disrupted track measurements.
- This breakthrough facilitates improved railway track integrity prediction.
- Enables more resilient railway operation management.
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