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


The train control system is the “nerve center” of the high-speed train information system, which is large-scale and comprises various components. The on-board subsystem is the core of the train control system and is key to ensuring traffic safety and improving operating efficiency. Currently, the fault data processing methods of the on-board subsystem remain manual, which primarily realizes the fault detection and location. It is difficult to achieve the fault mechanism level, and fault prediction cannot be realized effectively. In this paper, the on-board subsystem structure and the fault disposal status were analyzed. The existing problems have been summarized, and some concepts and algorithms to predict faults were introduced. Based on the on-board subsystem structure and each module's performance parameters, the system-level fault prediction model was established. Based on the practical operational data sets, the fault prediction based on the Bayesian network was carried out and verified under 20, 200, 2000 and 20000 sets, respectively. The prediction accuracy was 5%, 27%, 92% and 96.3%, respectively, under the condition of 2000 data sets. The hidden Markov model and neural network-based fault prediction solutions were compared with the proposed method. The results demonstrate the advanced performance of the Bayesian network-based solution in system-level fault prediction.

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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

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