Supervised contrastive learning-guided prototypes on axle-box accelerations for railway crossing inspections

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Increasing demands on railway structures have led to a need for new cost-effective maintenance strategies in recent years. Current dynamic railway track monitoring systems are usually based on the analysis of axle-box accelerations to automatically detect track singularities and defects. These methods rely on hand-crafted feature extraction and classifiers for different tasks. However, the low performance shown in previous literature makes it necessary to complement these analyses with in-situ inspections. Very recent works have proposed the use of deep learning systems that allow extracting more generalizable features from time–frequency spectrograms. However, the lack of specific public domain datasets and the finite number of track singularities in a railway structure have limited the development of deep learning based systems. In this paper, we propose a method capable of outstanding in low-data scenarios. In particular, we explore the use of supervised contrastive learning to cluster class embeddings nearly in the encoder latent space, which is used during inference for prototypical distance-based class assignment. We provide comprehensive experiments demonstrating the performance of our method in comparison to previous literature for detecting worn-out crossings.