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Predicting Track Geometry Using Machine-Learning Methods
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2023-10-06
[PDF-3.37 MB]
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Corporate Contributors:United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability. USDOT Tier 1
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Abstract:This study centers on predictive maintenance, an approach that foresees maintenance requirements based on anticipated defect occurrences. The research aims to create a model that accurately forecasts track geometry irregularities, empowering engineers to proactively address maintenance needs. Traditionally, maintenance decisions relied on experience, manual inspections, and cyclical upkeep, leading to potential safety concerns and cost escalations. This research takes a novel approach by integrating mechanical and data-driven models, utilizing functional networks, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. RNNs capture sequences effectively, while LSTM networks excel in tracing long-term dependencies, making them apt for predicting track degradation patterns.
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