Abstract
This paper presents the description of the basic principles of designing an Intelligent Decision Support System (IDSS) for diagnosing electrical equipment (EE) of industrial facilities while in operation based on the data received from the measurement technology using soft computing methods and their combinations, as well as fuzzy cognitive modeling. Since the development of an IDSS for diagnosing EE is a complex task that requires the study of a large number of interconnected modules, the work includes detailed information on the IDSS architecture, IDSS operating principles and basic capabilities of the system. By way of example, some objective-settings solved by the system, as well as fragments of screen forms of the developed system have been shown. The proposed IDSS will make it possible not only to assess the EE condition at a given time under conditions of a wide range of monitored parameters, but also to predict their values under conditions of statistical and fuzzy data. That will help to identify EE defects and failures at an early stage of their development; to prevent emergencies and reduce the risk of man-made disasters; to increase the validity of making decisions on EE faults and the equipment as a whole, as well as to give troubleshooting recommendations.
The work was supported by RFBR grants No. 19-07-00195, No. 19-08-00152.
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Kolodenkova, A.E., Vereshchagina, S.S., Muntyan, E.R. (2020). Development of an Intelligent Decision Support System for Electrical Equipment Diagnostics at Industrial Facilities. 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_23
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