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SCIENTIA SINICA Informationis, Volume 50 , Issue 4 : 496-510(2020) https://doi.org/10.1360/SSI-2019-0220

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

More info
  • ReceivedOct 7, 2019
  • AcceptedDec 7, 2019
  • PublishedApr 3, 2020

Abstract


Funded by

国家自然科学基金(61490703,61922042)


References

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  • Figure 1

    (Color online) Schematic diagram of traction systems in CRH2-type high-speed trains

  • Figure 2

    (Color online) Platform of fault injection and diagnosis for traction systems of high-speed trains

  • Figure 3

    (Color online) Detection results for 4 types of faults. (a) Fault $f_1$: fault detection ratio is $100%$, and false alarm ratio is $0.3%$; (b) fault $f_2$: fault detection ratio is $59.69%$, and false alarm ratio is $0.92%$; (c) fault $f_3$: fault detection ratio is $99.46%$, and false alarm ratio is $0.77%$; (d) fault $f_4$: fault detection ratio is $71.5%$, and false alarm ratio is $0.23%$

  • Figure 4

    (Color online) Diagnosis results using (a) traditional SVM and (b) the proposed modified SVM

  •   

    Algorithm 1 离线辨识算法

    根据给定的 $N,s$, 得到输入矩阵 $U$ 与输出矩阵 $Y$;

    通过式 (14), 得到 $\mathcal{M}$ 矩阵;

    依据式 (15), 定义数据驱动的残差信号 $r(k)$;

    通过式 (17) 定义统计量, 并根据式 (18) 得到用于故障检测的阈值.

  •   

    Algorithm 2 离线故障特征提取算法

    对于 $\mathcal{T}$ 种故障数据, 通过式 (15) 得到所有类型故障的残差信号;

    采用一对多法, 定义故障诊断的目标函数 (20);

    根据诊断误差, 更新权重向量 $m$;

    通过式 (23) 得到新的惩罚因子;

    生成新的故障诊断超平面.

  •   

    Algorithm 3 在线 FDD 算法

    读取在线数据 $u$ 和 $y$;

    根据式 (15) 得到当前时刻残差;

    依据式 (17) 中的统计量, 进行故障检测. 当系统无故障时, 返回第 1 步;否则继续执行算法;

    以报警的残差作为算法 2 得到分类器的输入, 输出故障类型, 并返回第 1 步.