Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring

2026-02-17

AI Summary

This work addresses the challenges of online wheelset fault detection in railways, where model performance degrades under varying operating conditions, reliance on handcrafted features persists, and labeled data are scarce. To this end, the authors propose a semantic-aware, label-efficient continual learning framework that integrates semantic metadata—such as axle count, wheel position, and strain—derived from accelerometer and fiber Bragg grating sensors. The approach combines unsupervised variational autoencoder representations with a lightweight gradient-boosting classifier and incorporates a replay-based continual learning mechanism. This is the first method to jointly optimize semantic metadata and unsupervised deep representations, enabling effective adaptation to unseen operational conditions with minimal labeled data while mitigating catastrophic forgetting. Experiments demonstrate accurate detection of subtle defects like spalls and polygonization, maintaining high stability across variations in train type, speed, load, and track conditions, using only a single accelerometer and strain gauge for deployment.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance frameworks increasingly leverage sensor-generated time-series data, yet traditional methods require manual feature engineering, and deep learning models often degrade in online settings with evolving operational patterns. This work presents a semantic-aware, label-efficient continual learning framework for railway fault diagnostics. Accelerometer signals are encoded via a Variational AutoEncoder into latent representations capturing the normal operational structure in a fully unsupervised manner. Importantly, semantic metadata, including axle counts, wheel indexes, and strain-based deformations, is extracted via AI-driven peak detection on fiber Bragg grating sensors (resistant to electromagnetic interference) and fused with the VAE embeddings, enhancing anomaly detection under unknown operational conditions. A lightweight gradient boosting supervised classifier stabilizes anomaly scoring with minimal labels, while a replay-based continual learning strategy enables adaptation to evolving domains without catastrophic forgetting. Experiments show the model detects minor imperfections due to flats and polygonization, while adapting to evolving operational conditions, such as changes in train type, speed, load, and track profiles, captured using a single accelerometer and strain gauge in wayside monitoring.

Problem

• wheel fault detection
• online continual learning
• wayside monitoring
• anomaly detection
• railway maintenance

Innovation

• continual learning
• sensor fusion
• variational autoencoder
• fiber Bragg grating
• unsupervised anomaly detection