Anomaly detection in railway infrastructure

Abstract

In order to keep complex railway systems fail-safe, sophisticated maintenance of the rolling stock and infrastructure are most essential. Although AI-based predictive maintenance systems exist in many different industries, there is still quite large potential for different application scenarios. The current research shows such an example, where machine learning can be applied to detect anomalies in the pantograph-catenary system by using a simple convolutional neural network that is able to detect arc ignitions during train operation. The paper provides some insights into the process of the system development life cycle. Starting from the initial idea to use machine learning for anomaly detection, over the system design of a prototype and the training of the Keras-based machine-learning model, up until the evaluation of the conducted experiments. The arcVision system prototype provides valuable insights into how a predictive maintenance process could be established by combining the results from the machine-learning model with rules and insights from manual inspections.