Within railway infrastructure, railway point systems are among the most critical equipment, not only due to accidents and delays caused by their failures but also due to maintenance costs. The detection of early signs of degradation and the ability to identify the maintenance actions required to prevent a failure are key aspects of a successful and advantageous health assessment strategy. While studies focusing on the detection and prognostics of railway point systems exist, few or none address the correlation between environment, field layout and the point system behavior. This paper aims to consider the interaction between these factors and the point system behavior, and compare a fleet-based approach to an asset-based approach for the point systems health assessment, highlighting the influence of the field configuration on the effectiveness of the two methods. The proposed methods exploit Self-Organizing Maps (SOMs) to construct a health indicator for both the detection and the diagnosis of railway point systems. The approaches are applied to a case study for the on-line health assessment of 20 electro-mechanical point systems operating on a main line over the course of 6 months. The results show how an asset-based monitoring system is necessary in order to maintain a level of information which enables to achieve an efficient detection of anomalies and a correct identification of degradation mechanisms. In addition, fleet-based health assessment leads to a higher percentage of missed alarms, due to the intrinsic hypothesis of considering all point systems as operating in the same context and mission profile.
How to Cite
anomaly detection, PHM, Self-organizing maps, Railway turnouts, fleet-based, asset-based
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