Data Driven Anomaly Control Detection for Railway Propulsion Control Systems

Sammanfattning: The popularity of railway transportation has been on the rise over the past decades, as it has been able to provide safe, reliable, and highly available service. The main challenge within this domain is to reduce the costs of preventive maintenance and improve operational efficiency. To tackle these challenges, one needs to investigate and provide new approaches to enable quick and timely data collection, transfer, and storage aiming at easier and faster analysis whenever needed. In this thesis, we aim at enabling the monitoring and analysis of collected signal data from a train propulsion system. The main idea is to monitor and analyze collected signal data gathered during the regular operation of the propulsion control unit or data recorded during the regular train tests in the real-time simulator. To do so, we have implemented a solution to enable train signal data collection and its storage into a .txt and .CSV file to be further analyzed in the edge node and in the future connected to the cloud for further analysis purposes. In our analysis, we focus on identifying signal anomalies and predicting potential failures using MathWorks tools. Two machine learning techniques, unsupervised and supervised learning, are implemented. Additionally, in this thesis, we have investigated ways of how data can be efficiently managed. We have also reviewed existing edge computing solutions and anomaly detection approaches using a survey as a suitable method to identify relevant works within the state of the art.