Authors A.N. Shabelnikov, S.M. Kovalev, A.V. Sukhanov
Month, Year 08, 2016 @en
Index UDC 656.212.5
DOI 10.18522/2311-3103-2016-8-6878
Abstract Nowadays, a wide implementation of automation tools into the railway transportation sys-tems occurs. One of the important sides of this process is automation of the control for and trains makeup-breaking-up at hump yards in real-time mode with utilization of modern intelligent tech-nologies. “Intelligence incorporation” for technological processes, firstly, deals with the problem of decision-making support in complicated technological situations and, particularly, in abnormal situations, which referred to the faults in the process behavior. Here, the most important task is the immediate detection of such situations, which aim is to prevent faults and adjust the process. The paper proposes a new approach for preventive detection of abnormal situations in the behavior of the technological process of train breaking-up at hump yards, when the faults are related to over-taking of cuts. The proposed approach is based on the change of the method of fault prediction by the method of precursor detection. The detection method is based on granular model of process representation in form of specific temporal patterns. Granular model of technological process is formed on the basis of fuzzy estimates inference and fuzzy estimates postprocessing by using the tools of pragmatic logic. Precursors detection for the granular model is performed by using the model of multistep prediction. Here, multistep prediction is performed by utilization of Markov reward models tuned by temporal-difference learning algorithm. Computational experiments show that presented approach allows improving the efficacy of the decision making at hump yards. Moreover, it should be noted that proposed models and algorithms are usable for many other tasks, where decision making in uncertain conditions is required.

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Keywords Breaking-up process; overtaking of cars; granular model; cognitive measurements; anoma-lous temporal pattern; stochastic model; temporal model.
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