Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
This work addresses predictive maintenance for railway safety, offering a cost-effective solution for wayside monitoring, though it appears incremental as it combines existing techniques like VAEs and gradient boosting with continual learning.
The paper tackles online continual wheel fault detection in railway monitoring by developing a semantic-aware continual learning framework that fuses accelerometer data with metadata from fiber Bragg grating sensors, achieving detection of minor imperfections like flats and polygonization while adapting to evolving operational conditions.
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.