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arXiv · 2504.08554

Boosting-inspired online learning with transfer for railway maintenance

Diogo Risca·Afonso Lourenço·Goreti Marreiros
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Abstract

The integration of advanced sensor technologies with deep learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface. Although numerous models have been proposed to detect irregularities such as wheel out-of-roundness, they often fall short in real-world applications due to the dynamic and nonstationary nature of railway operations. This paper introduces BOLT-RM (Boosting-inspired Online Learning with Transfer for Railway Maintenance), a model designed to address these challenges using continual learning for predictive maintenance. By allowing the model to continuously learn and adapt as new data become available, BOLT-RM overcomes the issue of catastrophic forgetting that often plagues traditional models. It retains past knowledge while improving predictive accuracy with each new learning episode, using a boosting-like knowledge sharing mechanism to adapt to evolving operational conditions such as changes in speed, load, and track irregularities. The methodology is validated through comprehensive multi-domain simulations of train-track dynamic interactions, which capture realistic railway operating conditions. The proposed BOLT-RM model demonstrates significant improvements in identifying wheel anomalies, establishing a reliable sequence for maintenance interventions.

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Diogo Risca, Afonso Lourenço, Goreti Marreiros. 2025-04-11. Boosting-inspired online learning with transfer for railway maintenance. https://arxiv.org/abs/2504.08554

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