APPLICATION OF NEURAL NETWORKS FOR SOLVING PROBLEMS OF FORECASTING TECHNICAL CONDITIONS OF ELECTRIC POWER EQUIPMENT
Relevance This article is devoted to the development of a subsystem for forecasting the reliability of maintenance and repair systems in the electric power industry. The reliability forecasting tasks are more actual nowadays than ever before because they give an answer to the question about expediency of further expenditures, necessary for technology processing and production of maintenance and repair systems in electric power industry. Adequate modeling of reliability of maintenance and repair systems in electric power industry can be realized only by means of automation. Aim of research To analyze the subject area, to develop mathematical and information support. Research methods The main for reliability analysis are structural methods, which allow to represent the system in the form of a structural diagram describing the logical relationships between the states of the elements and the system as a whole, taking into account the structural and functional relationships and the interaction of elements. Results The methods of calculating the reliability of maintenance and repair systems in the electric power industry are investigated, the software that allows you to analyze the reliability using neural networks is implemented. The result of the work is a program for forecasting the reliability of maintenance and repair systems in electric power industry of any structure with the help of neural networks.