数据驱动高速列车动态牵引系统的故障诊断

Data-driven fault diagnosis for dynamic traction systems in high-speed trains

牵引系统为高速列车的重要组成部分,其可靠性对列车安全运行至关重要.本文利用牵引系统传感器数据,提出了一种最优的数据驱动故障检测与诊断(fault detection and diagnosis, FDD)方法,用于解决动态牵引系统的故障诊断问题.首先,基于传感器数据构建系统模型,用于描述牵引系统动态.然后,通过相关性与子系统辨识技术,定义残差生成器以及故障检测统计量.而后根据改进的支持向量机(support vector machine, SVM),研究了最优的数据驱动故障诊断问题.最后,通过中车株洲电力机车研究所有限公司的高速列车实验平台,验证了所提出方法的合理性与有效性.

Traction systems are an important aspect of high-speed trains, and their reliable operation is crucial.With data available from trains, this paper proposes an optimal fault detection and diagnosis(FDD) strategy for dynamic traction systems. Based on the established dynamic model, using sensor measurements, a correlationaided subspace identification technique is proposed to formulate residual signals and corresponding test statistics for fault detection. Then, a modified support vector machine(SVM) is designed for optimally solving the diagnosis bias caused by the difference in the apparent probabilities of multiple fault scenarios. The feasibility and effectiveness of the proposed optical FDD performance are illustrated in the CRRC experimental platforms.