Abstract
This paper presents a methodology to support decision making based on the tram wheel-rail interface condition. The methodology relies on the following measurements: tram failure log-files regarding wheel-sliding events, monitored acoustics data and open source weather information. The proposed methodology consists of three stages: 1) data collection and pre-processing, 2) spatial analysis based on clustering, and 3) decision support based on the extracted information. For clustering, the Density-Based Algorithm (DBSCAN) is used for the analysis of wheel-sliding events. Self-organizing maps (SOMs) are employed for the analysis of acoustics data. A real-life case study is used to show how use of the methodology can find interesting hotspots that are candidates for further monitoring and maintenance actions. The measurements were obtained from the tram system in the city of Rotterdam, The Netherlands.
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