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Decision support tool based on multi-source data analysis for the tram wheel-rail interface

机译：基于多源数据分析的电车轮轨接口决策支持工具

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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.
机译：本文提出了一种基于电车轮轨接口条件的决策支持方法。该方法基于以下测量：有关车轮滑动事件的电车故障日志文件，监控的声学数据和开源天气信息。所提出的方法包括三个阶段：1）数据收集和预处理; 2）基于聚类的空间分析; 3）基于提取的信息的决策支持。对于聚类，基于密度的算法（DBSCAN）用于分析车轮滑动事件。自组织图（SOM）用于分析声学数据。实际案例研究显示了该方法的使用如何可以找到有趣的热点，这些热点是进一步监视和维护操作的候选对象。这些测量值是从荷兰鹿特丹市的有轨电车系统获得的。

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