Abstract
This paper proposes novel methods for anomaly detection in industrial dynamic processes based on logical and algebraical approaches. The paper contains an overview of some intensively developed approaches to dynamic processes modeling in terms of their suitability for the detection of anomalies. Main models of industrial dynamic processes using classical theory of stability, models with Pareto distribution, wavelet analysis, the problem of change-point, models with data mining methods are considered. Two logical approaches with discrete time are proposed: first one is using classical predicate logic and second one is using dynamic description logic.
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The reported study was funded by the Russian Foundation for Basic Research, projects № 19-07-00329-a, 18-08-00549-a.
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Ilicheva, V.V., Guda, A.N., Shevchuk, P.S. (2020). Logical Approaches to Anomaly Detection in Industrial Dynamic Processes. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_36
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