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SCIENTIA SINICA Informationis, Volume 50 , Issue 4 : 511-526(2020) https://doi.org/10.1360/SSI-2019-0227

Research on the fault prediction method of an on-board subsystem in high-speed train control systems

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  • ReceivedOct 15, 2019
  • AcceptedJan 24, 2020
  • PublishedApr 13, 2020

Abstract


Funded by

国家自然科学基金重大项目(61490705)


References

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  • Figure 1

    The structure of 300T on-board subsystem in train control system

  • Figure 2

    BN-based fault prediction

  • Figure 3

    (Color online) Faults produced in 2015 by different modules of on-board subsystem in train control system

  • Figure 4

    Fault model of on-board subsystem

  • Figure 5

    Simplified fault model of on-board subsystem

  • Figure 6

    (Color online) Result of BN structure learning. (a)–(d) Sample = 2, 20, 200, 2000, respectively

  • Figure 7

    (Color online) Results of signal test point parameter learning. (a) Energy carrier signal; (b) TCR working voltage; (c) SDU working voltage; (d) VDX working voltage; (e) radio operating frequency

  • Figure 8

    (Color online) Results of fault prediction based on the BN. (a)–(d) Sample = 20, 200, 2000, 20000, respectively

  • Figure 9

    Result of fault prediction based on the HMM

  • Figure 10

    (Color online) Result of fault prediction based on the NN. (a) Training confusion matrix; (b) validation confusion matrix; (c) testing confusion matrix; (d) final confusion matrix

  • Table 1   Test points in functional units
    Functional unit Test point Parameter
    (1) $t_1$ BTM working voltage
    $t_2$ Energy carrier signal
    $t_3$ Up-link signal
    $t_4$ Power sense signal
    (2) $t_5$ SDU working voltage
    $t_6$ ODO working voltage
    $t_7$ Radar working voltage
    (3) $t_8$ TCR working voltage
    $t_9$ Antenna frequency
    (4) $t_{10}$ VDX working voltage
    $t_{11}$ Mining voltage
    (5) $t_{12}$ STU-V working voltage
    $t_{13}$ Radio operating frequency
  • Table 2   Bayesian nodes and discrete values
    Discrete value State $t_2$ $t_5$ $t_8$ $t_{10}$ $t_{13}$ $F_1$
    1 Normal 27.095 (MHz) 18 (V) 110 (V) 24$\sim$120 (V) 915 (MHz) 1
    2 Deviation $[ - 5\% ,~ + 5\% ]$ $[ - 5\% ,~ + 5\% ]$ $[ - 30\% ,~ - 25\% ]$ $[ - 25\% ,~ + 30\% ]$ $[ - 5\% ,~ + 5\% ]$
    3 Abnormal $< - 5\% ,~ > 5\%$ $< - 5\% ,~ > 5\%$ $ < - 30\% ,~ > 25\% $ $< - 25\% ,~ > 30\%$ $< - 5\% ,~ > 5\%$ 2
  • Table 3   Predict accuracy under different algorithms
    Algorithm Prediction accuracy (2000 set) (%)
    Hidden Markov model 80.1
    Neural network 67.4
    Bayesian network 92