Development of a Program Complex for Diagnostics of Electrical Engineering Systems Under Conditions of Heterogeneous Data

This paper presents the structure of a program complex for diagnosing electrical systems (ES), specifically electrical equipment (EE) under conditions of heterogeneous data based on the principle of integrated modular technology. This complex is developed to prevent ES from premature failure, as well as to identify the methods and ways to solve them in real time. The principle of building such a complex is realized by implementation of the developed methods and models for decision support using methods and technologies of artificial intelligence. An important feature of realization and application of the program complex is the ability to function (training) and perform diagnostics of ES technical state in real time mode under conditions of incomplete and fuzzy information. Scientific novelty of the result is the building of a simplified complex that represents all the requirements from regulatory documentation, equipment passports, which combine interrelated cognitive, non-parmetric, fuzzy models in combination with methods for measuring the state of the equipment under conditions of uncertain initial data presented in the form of verbal descriptions, intervals of acceptable measurements and fuzzy triangular, trapezoidal numbers. When diagnosing ES, the complex will create a database and knowledge base of models for proper functioning of systems and equipment with subsequent recognition of faulty states and their classification, as well as provide recommendations to prevent their failure. The proposed program complex can be part of intelligent decision support systems for diagnostics of any ES and EE, as well as be used separately. It provides increasing informativeness of decision-making situations, reliability of conclusions about the technical state of ES and EE under conditions of incomplete and fuzzy information, as well as reducing the time to assess the ES state and make decisions about its condition.