Scalable, Technology-Agnostic Diagnosis and Predictive Maintenance for Point Machine using Deep Learning

TL;DR

This paper presents a scalable, deep learning-based method for diagnosing and predicting failures in railway point machines using only power signal data, achieving high accuracy and confidence, and applicable across different technologies.

Contribution

The authors introduce a technology-agnostic deep learning approach that requires only one input signal for failure detection and prediction in point machines, enhancing scalability and standard compliance.

Findings

Achieved >99.99% precision in failure classification

Reduced false positive rate to <0.01%

Validated scalability across multiple PM types in real-world and test environments

Abstract

The Point Machine (PM) is a critical piece of railway equipment that switches train routes by diverting tracks through a switchblade. As with any critical safety equipment, a failure will halt operations leading to service disruptions; therefore, pre-emptive maintenance may avoid unnecessary interruptions by detecting anomalies before they become failures. Previous work relies on several inputs and crafting custom features by segmenting the signal. This not only adds additional requirements for data collection and processing, but it is also specific to the PM technology, the installed locations and operational conditions limiting scalability. Based on the available maintenance records, the main failure causes for PM are obstacles, friction, power source issues and misalignment. Those failures affect the energy consumption pattern of PMs, altering the usual (or healthy) shape of the power signal during the PM movement. In contrast to the current state-of-the-art, our method requires only one input. We apply a deep learning model to the power signal pattern to classify if the PM is nominal or associated with any failure type, achieving >99.99\% precision, <0.01\% false positives and negligible false negatives. Our methodology is generic and technology-agnostic, proven to be scalable on several electromechanical PM types deployed in both real-world and test bench environments. Finally, by using conformal prediction the maintainer gets a clear indication of the certainty of the system outputs, adding a confidence layer to operations and making the method compliant with the ISO-17359 standard.