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A PROGNOSTICS AND HEALTH MANAGEMENT (PHM) SYSTEM FOR ELECTROMECHANICAL BRAKING (EMB) SYSTEMS IN TRAINS

机译:火车机电制动(MEM)系统的预测和健康管理(PHM)系统

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Electromechanical brake (EMB) is a novel braking mode for railway trains. The reliability of the braking system is important for railway system safety. According to the RAMS (Reliability, Availability, Maintainability and Safety) requirements for railway applications, the key issues of prognostics and health management (PHM) for EMB systems are discussed at first. Consequently, the dominant tasks of the PHM system are confirmed, containing the battery State-of-Charge (SOC) and State-of-Health (SOH) estimation, electric components condition monitor, and mechanical crack prediction. Then the critical failure modes of the EMB system and their failure mechanisms are analyzed. Based on the above analysis, a PHM system developed for EMB systems and its working flow are introduced. The vehicle operation parameters, the brake control commands, and the sensor signals are inputs of the PHM system. These inputs are processed and gathered as health indicators. Then the PHM system adopts the physical model or the hybrid algorithms to track the failure mode and components. Finally, the PHM system locates the health stage of the EMB system. The primary health indicators for EMB systems are the braking distance and emergency battery capacity. And the health indicators for components are mapped with the corresponding failure modes. The estimation for the battery SOC and SOH is established based on the test results of battery properties. The model-based and data-driven hybrid method is utilized to detect the crack growth of mechanical components and the degradation in electric properties. The PHM system is useful for condition-based maintenance. And it is meaningful for the reliability and safety improvement of the EMB systems.
机译:机电制动器(MEM)是铁路列车的新型制动模式。制动系统的可靠性对于铁路系统安全性很重要。根据RAMS(可靠性,可用性,可维护性和安全性)对铁路应用的要求,首先讨论了对MEM系统的预后和健康管理(PHM)的关键问题。因此,确认了PHM系统的主导任务,包含电池充电(SOC)和健康状态(SOH)估计,电气分量条件监测器和机械裂纹预测。然后分析了BEM系统的临界失效模式及其故障机制。基于上述分析,介绍了一种为BEM系统开发的PHM系统及其工作流程。车辆操作参数,制动控制命令和传感器信号是PHM系统的输入。这些输入被处理并收集为健康指标。然后,PHM系统采用物理模型或混合算法来跟踪故障模式和组件。最后,PHM系统定位了BEM系统的健康阶段。 MEM系统的主要健康指标是制动距离和紧急电池容量。组件的健康指标用相应的故障模式映射。基于电池特性的测试结果建立了电池SOC和SOH的估计。基于模型和数据驱动的混合方法用于检测机械部件的裂纹生长和电性能下降。 PHM系统对基于条件的维护有用。并且它对MEB系统的可靠性和安全性有意义。

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