Model of efficiency assessment of diagnostic tools of onboard equipment

The Aim of this paper is to show that the development, deployment of new diagnostic tools and improvement of the existing diagnostic tools in onboard equipment enables better operational characteristics and reduced probability of transition of intelligent railway systems into a forbidden state.

Method. In the context of intelligent railway systems, the construction of the analytical model of probability evaluation is of principal interest due to the feasibility of demonstrating the factors that are taken into consideration by such a model. Forbidden events that cause inoperability of intelligent railway systems are random; they can be represented as a random process. A random process of system development, transition from an allowed state into a forbidden state, system state changes in time can be described with a semi-Markovian process. When assessing the probability of system transition into a forbidden state, the question arises as to the selection of a method of calculation. The paper shows the feasibility of representation and solution of a semi-Markovian model with the help of a coupled graph model [3, 5] that has a high level of visualization and is a well-formalized method of identification of the probability of a system’s transition into a forbidden state. The set of system states and their connections are represented with a directed state graph with defined topological concepts [3]. In order to identify the effect of the introduction of new diagnostic tools and improvement of the existing diagnostic tools in onboard equipment on the probability of transition of intelligent railway systems into a forbidden state, the authors use the theorem of identification of the probability of system’s transition from the initial unhazardous state into a hazardous state and set forth the formula to calculate this probability.

Results. The graph method implemented in this paper shows that the use of additional diagnostic tools reduces more than twice the probability of a system’s transition into a forbidden state, i.e. a state when the failure will not be detected by the inbuilt or additional diagnostic tools.

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