国家自然科学基金重大项目(61490705)
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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
| 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 |
| 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 |
| Algorithm | Prediction accuracy (2000 set) (%) |
| Hidden Markov model | 80.1 |
| Neural network | 67.4 |
| Bayesian network | 92 |
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