Fault Diagnosis of Track Circuit Compensation Capacitor Based on GWO Algorithm

Zicheng Wang, Lifu Yi, Kai Yu, G. Gu, Jianqiang Wang
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引用次数: 1

Abstract

Track circuit (TC) is an important equipment in China Train Control System (CTCS). But its failure rate has been high. The predictive maintenance mechanism of TC can further ensure the safe operation of train. While the fault diagnosis and realization of TC status is the premise to achieve prediction maintenance. With regards to this, a fault diagnosis method for TC based on Grey Wolf Optimizer (GWO) algorithm is put forward in this paper. A Uniform Transmission Line (UTL) Model of TC is established and the impact on the Locomotive Signal Amplitude Envelope (LSAE) by ballast resistance, compensation capacitor is analyzed. The above parameters are chosen as the decision variables. To minimum the difference between the real LSAE and the one calculated using the UTL model as the objective to form the fitness function. GWO algorithm has the characteristic of insensitivity to initial solution values, higher optimization efficiency and global optimization ability and it is employed to iteratively search for the optimum solution of TC parameters. Experiment results show that the method proposed in this paper can realize the diagnosis of important parameters of TC and it has high adaptability and excellent accuracy.
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基于GWO算法的轨道电路补偿电容故障诊断
轨道电路是中国列控系统(CTCS)的重要设备。但它的失败率一直很高。TC的预测性维护机制可以进一步保障列车的安全运行。而故障诊断和TC状态的实现是实现预测维护的前提。针对这一问题,本文提出了一种基于灰狼优化算法(GWO)的TC故障诊断方法。建立了机车均匀传输线(UTL)模型,分析了镇流器电阻、补偿电容对机车信号幅值包络线(LSAE)的影响。选择上述参数作为决策变量。以使实际LSAE与使用UTL模型计算的LSAE之差最小为目标,形成适应度函数。GWO算法对初始解值不敏感,具有较高的优化效率和全局优化能力,可用于迭代搜索TC参数的最优解。实验结果表明,本文提出的方法能够实现对TC重要参数的诊断,具有较高的适应性和较好的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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