TEDS fault recognition technology based on multi-source data analysis
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摘要: 单点运行的动车组运行故障动态图像检测系统（TEDS）故障自动识别功能存在识别准确率不足，误判率高的问题。为此，提出了一种基于多源数据的动车组故障图像识别方法，以联网运行的TEDS数据为基础，结合传统的差异检测法，对不同空间与时间TEDS采集的同一列车图像进行多源数据融合与权重差异计算，实现了动车组车体异常部位的检测。试验表明，该方法建立了更为准确的对比参考源，减少了环境对成像内容的影响，能够提高动车组运行故障自动识别率，降低误报率。Abstract: The automatic fault recognition function for TEDS (Trouble of moving EMU Detection System) of singlepoint operation has the problems of insufficient recognition accuracy and high misjudgment rate. This paper proposeda fault image recognition method for EMU based on multi-source data. Based on TEDS data of network operationand combined with traditional difference detection method, the paper carried out the multi-source data fusion andweight difference calculation for the same train image collected by TEDS in different space and time, and implementedabnormal parts detection of EMU car body. Experiments show that the proposed method establishes a more accuratecomparative reference source, reduces the impact of the environment on the image content, improves the automaticrecognition rate of EMU operation fault, and reduces the false alarm rate.
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