Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects
This addresses the problem of cost-effective rail track assessment for railway operators, though it is incremental as it builds on existing transformer-based approaches with a focus on computational efficiency.
This study tackled the challenge of detecting small,2-10 cm transient defects in rail infrastructure using indirect structural health monitoring with onboard sensors, achieving accuracy comparable to state-of-the-art solutions while demonstrating better inference speed.
Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting small, transient anomalies (2-10 cm) remains a significant challenge due to complex vehicle dynamics, signal noise, and the scarcity of labeled data limiting supervised approaches. This study addresses these issues through unsupervised deep learning. We introduce an incremental synthetic data benchmark designed to systematically evaluate model robustness against progressively complex challenges like speed variations, multi-channel inputs, and realistic noise patterns encountered in iSHM. Using this benchmark, we evaluate several established unsupervised models alongside our proposed Attention-Focused Transformer. Our model employs a self-attention mechanism, trained via reconstruction but innovatively deriving anomaly scores primarily from deviations in learned attention weights, aiming for both effectiveness and computational efficiency. Benchmarking results reveal that while transformer-based models generally outperform others, all tested models exhibit significant vulnerability to high-frequency localized noise, identifying this as a critical bottleneck for practical deployment. Notably, our proposed model achieves accuracy comparable to the state-of-the-art solution while demonstrating better inference speed. This highlights the crucial need for enhanced noise robustness in future iSHM models and positions our more efficient attention-based approach as a promising foundation for developing practical onboard anomaly detection systems.