牵引系统支撑电容PHM技术研究

Research on PHM technology of traction system support capacitor

支撑电容作为轨道车辆牵引系统的核心部件之一,支撑电容发生故障将严重影响着列车安全稳定运行,因此研究支撑电容故障预测及健康管理(PHM)技术,实现支撑电容健康状态在线预测具有十分重要的意义。首先对支撑电容结构设计、工作特性及老化机理进行了深入的研究,将容值和ESR值退化率作为支撑电容失效判据,然后提出了基于数理统计+多项式回归算法,构建电容容值和ESR值软测量模型,利用大量试验数据进行模型训练及模型优化,最后从软测量误差和数理统计分布一致性两个角度对模型软测量结果准确度进行了评估,试验结果表明数理统计+多项式回归软测量模型在不同样本集和不同工况下,能够对支撑电容容值和ESR值进行有效软测量,验证了该模型的可行性和准确性。

As one of the core components of the traction system of rail vehicles, the failure of the supporting capacitor will seriously affect the safe and stable operation of the train. Therefore, it is of great significance to study the fault prognostics and health management(PHM) technology of the support capacitor and realize the online health state prediction of the support capacitor. Firstly, the structure design, working characteristics and aging mechanism of the supporting capacitor are deeply studied, and the degradation rate of capacitance value and ESR value is taken as the failure criterion of the support capacitor. Then, a soft sensing model of capacitance value and ESR value is constructed based on mathematical statistics and polynomial regression algorithm. A large number of test data are used to train and optimize the model, Finally, the accuracy of the soft sensing results is evaluated from two aspects of the soft sensing error and the consistency of mathematical statistics distribution. The experimental results show that the mathematical statistics + polynomial regression soft sensing model can effectively soft sensing the support capacitance and ESR values in different sample sets and different working conditions, which verifies the feasibility and accuracy of the model.