Earticle

China Train Control System (CTCS-3) is a safety critical computer system that features large-scale, complex structure, and redundant configuration. CTCS-3 and as well as the equipment and technologies related to it can ensure the safety and reliability running of high-speed trains, and so the assessment on its reliability becomes very important. Generally, the two elements such as common cause factor (CCF) and failure mode polymorphism can not be ignored when assessing the reliability on a large complicated system. There are some limitations existing when applying the traditional fault tree analysis (FTA) is used to deal with the two factors mentioned above, and considering that Bayesian network (BN) possesses the abilities of bidirectional reasoning and the uncertain knowledge solving, and therefore BN is introduced to change the flaws. The method firstly establishes the FTA model of system from top to bottom, and then converts the FTA model into BN model from the lower to the upper, hierarchically. Eventually, the reliability indices are calculated using the BN with regard to the CCF and multi-state factors. Through integrating and evaluating sub-models using the approach with FTA combined with BN in the CTCS-3 system, some interesting results are acquired. The relative results show that the proposed approach is quite effective, and also provides a theoretical basis to improve the system reliability.

At present, high-speed railway is faster and faster, which puts forward higher safety requirements for high-speed railway, and the on-board equipment for CTCS-3 train control system is vital. Therefore, it is necessary to carry out real-time monitoring of the various parts of the on-board equipment in order to find the fault, isolate the fault and eliminate the fault and improve the safety of high-speed train. this paper presents the on-board equipment Prognostics and health management (PHM) research, the use of intelligent diagnosis algorithm and prediction techniques, according to the health assessment of residual life prediction and the on-board equipment for train control system state maintenance and condition based maintenance (CBM) provide a theoretical basis. Taking the GSM-R receiving station of the train control system as an example, it is concluded that the scoring standard and health index(HI)are more reliable for fault prediction and health management.