Railway wheels are safety critical components, have a significant impact on the performance and are a major cost driver for maintenance. The condition of the wheels has also a significant influence on the infrastructure condition and its maintenance. Furthermore, wheel defects cause noise and vibration emissions.
Due to their criticality, wheels are tightly monitored by different condition monitoring devices: including fixed installations, wayside monitoring devices and in‐workshop inspections. While wheel defect detection with wayside monitoring devices belongs to the state of the art, particularly based on strain gauges, prediction of the wheel deterioration and defect evolution in time under varying operating conditions is still an open research question.
Even though wheel‐rail interaction has been studied comprehensively for decades, it is still not fully understood under real operating conditions and several influencing parameters are not able to be included in the existing models. Particularly the introduction of high‐strength wheel and rail steels has imposed new challenges on the wheel‐rail interaction. The lack of understanding and modelling of causal relationships can be observed in the fact that different fleets with similar design and similar operating profiles experience different degradation and defect evolution.
The goal of the proposed research project is to predict the evolution of the wheel condition in time by integrating the information of several heterogeneous data sources including real‐time information on wheel condition and influencing parameters of its deterioration. The proposed methodology is based on deep learning algorithms enabling to learn the relevant features and their relationships from the heterogeneous data sources and use the learnt relationships to predict the profile evolution.
The proposed research project will advance the state of knowledge in the field of railway wheel deterioration prediction. The project has a high practical relevance by decreasing maintenance costs, increasing safety and improving planning ability and resource usage.
Prof. Dr. Olga Fink
Institute of Construction & Infrastructure Management
11.2018 (36 months)