Reliability assessment of CTCS-3 using Bayesian networks

Abstract

Train control system of high speed railway is the core equipment and technology to ensure the safe and reliable operation of high-speed trains, so reliability assessment of train control system counts for much. In this paper, firstly, Bayesian Networks (BN) technology is introduced for the limitation of traditional fault tree analysis (FTA) method. Secondly, reliability model of CTCS-3 train control system is established by BN transformed from the fault tree (FT) of CTCS-3. Finally, reliability of CTCS-3 and its redundant configuration is assessed by BN's advantage of expressing uncertainty knowledge and its bidirectional reasoning ability. It is not only convenient to identify the failure combination mode leading to the failure of the train control system, but also be able to find out the weak links in CTCS-3 system. Through the case analysis, BN shows great superiority in reliability assessment of system compared with FTA. What is more, it provides a new foundation for reliability assessment of train control system.

Advances in Fault Diagnosis for High-Speed Railway: A Review

“…The probability theory, Bayesian Network (BN), text mining, and deep learning technology have been widely used for fault diagnosis of the control and signaling system in HST. In [36], a combination of Fault Tree Analysis and BN was adopted to evaluate the failure rate of the HST control system as well as its subsystems, and analyze the impact of any one or more subsystem failures on system failure. [37] proposed a text mining based fault diagnosis method for VOBE of HST, in this paper, the topic model was adopted to extract fault features from the maintenance data, and a BN was used to adapt the uncertainty and complexity of VOBE fault diagnosis.…”

Reliability analysis on ATP system of CTCS-3 based on D-S evidence inference and Bayesian network

“…Furthermore, we assume that the components of ATP obey the exponential distribution. The code, name and failure rate of the basic units in the fault tree of ATP are shown by Table 1, which come from the reference [15]. …”

Stochastic Model Analysis and Control Strategy Design on Preventive Maintenance Based on Condition

“…199 In Figure 1, the symbol Si expresses the state of the equipment, where the subscript i ∈ [1,2,3,4] is applied to respectively express the equipment storage, work, preventive storag equipment; and v1 presents the failure rate of a stored equipment; and v2 presents the failure rate of a functioning equipment; and u1 presents the check rate of a stored equipm the stored probability of the device after a preventive maintenance intervention; and 1-interven maintenance intervention; and 1-e transferred into corrective maintenance state. The physical meaning of the transfer rate is to ensure that it is the bounded and the nonnegative.…”

“…With the rapidly development of condition based preventive maintenance (CBM) technology, CBM has been broadly applied in modern large-size in industry enterprises to instruct their operation maintenance practices to ensure the safety of production processes, for example, wind power plants, nuclear power stations, and railway transportation facilities [1][2][3]. In particular, wind power plants, which locate in the relatively remote regions with large-area distribution of the generators and abominable climate environment, often require a fireheight-ascending maintenance for wind turbines.…”