<ul><li>The integration of advanced sensor technologies with deep learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface.</li><li>This paper introduces BOLT-RM (Boosting-inspired Online Learning with Transfer for Railway Maintenance), a model designed to address the challenges in railway maintenance using continual learning for predictive maintenance.</li><li>BOLT-RM overcomes the issue of catastrophic forgetting that often plagues traditional models by allowing the model to continuously learn and adapt as new data become available, retaining past knowledge while improving predictive accuracy.</li><li>The proposed BOLT-RM model demonstrates significant improvements in identifying wheel anomalies, establishing a reliable sequence for maintenance interventions.</li></ul>

Boosting-inspired online learning with transfer for railway maintenance

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