Funding entity Union Internationale des Chemins de Fer (UIC)
The main goal of this project is the definition of a set of guidelines for components of overhead cable and traction power substations for the identification of symptoms of anomalies that could produce some failure modes, how to diagnose their causes, and what could be the recommendations from a maintenance point of view to prevent or mitigate the effect of these failure modes. In this study, different algorithms based on data are considered to alert of any abnormal behavior observed and assess the health conditions of components to propose a data-driven maintenance approach
Layman's summary: The main goal of this project is the definition of a set of guidelines for components of overhead cable and traction power substations for the identification of symptoms of anomalies that could produce some failure modes, how to diagnose their causes, and what could be the recommendations from a maintenance point of view to prevent or mitigate the effect of these failure modes. In this study, different algorithms based on data will be considered to alert of any abnormal behavior observed and assess the health conditions of components to propose a data-driven maintenance approach
Techniques employed: Reliability engineering, maintenance models, machine learning, anomaly detection, electrical system component diagnostics
UIC_MTO
