Augmenting train maintenance technicians with automated incident diagnostic suggestions

TL;DR

This paper presents a machine learning system that assists train maintenance technicians by providing automated diagnostic suggestions based on incident data, improving response times and diagnostic accuracy.

Contribution

It introduces a novel ensemble classifier for incident diagnosis using physically plausible event sets and incorporates a feedback loop for continuous model refinement.

Findings

Model trained and validated on real operational data

System deployed on a cloud platform for real-time assistance

Potential to enhance predictive maintenance and incident prevention

Abstract

Train operational incidents are so far diagnosed individually and manually by train maintenance technicians. In order to assist maintenance crews in their responsiveness and task prioritization, a learning machine is developed and deployed in production to suggest diagnostics to train technicians on their phones, tablets or laptops as soon as a train incident is declared. A feedback loop allows to take into account the actual diagnose by designated train maintenance experts to refine the learning machine. By formulating the problem as a discrete set classification task, feature engineering methods are proposed to extract physically plausible sets of events from traces generated on-board railway vehicles. The latter feed an original ensemble classifier to class incidents by their potential technical cause. Finally, the resulting model is trained and validated using real operational data and deployed on a cloud platform. Future work will explore how the extracted sets of events can be used to avoid incidents by assisting human experts in the creation predictive maintenance alerts.