Augmenting train maintenance technicians with automated incident diagnostic suggestions
This work addresses the need for faster and more efficient incident diagnosis for train maintenance technicians, though it is incremental as it builds on existing classification methods.
The paper tackles the problem of manually diagnosing train operational incidents by developing a learning machine that suggests diagnostics to maintenance technicians, which was deployed in production and validated using real operational data.
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.