Abstract
Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment.
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This work was financially supported by Russian Foundation for Basic Research, Grant No. 17-08-00402.
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Korobkin, V.V., Kolodenkova, A.E. (2019). Diagnosis in SEMS Based on Cognitive Models. In: Gorodetskiy, A., Tarasova, I. (eds) Smart Electromechanical Systems. Studies in Systems, Decision and Control, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-319-99759-9_22
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