APPLICATION OF ARTIFICIAL NEURON NETWORKS AND FUZZY LOGIC IN DIAGNOSTIC AND FORECASTING THE TECHNICAL CONDITION OF TRACTION MOTORS

Abstract

The article is devoted to the application of artificial intelligence in diagnosing and predicting the technical condition of electric motors. Currently, a number of traditional and modern methods are used to perform diagnostic monitoring of electric motors, and research is being conducted in this field. The application of traditional diagnostic monitoring systems in the diagnosis of motors faces problems in determining the normal and threshold values of the diagnostic parameters that cannot be measured in the working condition due to the lack of uncertain information. Various traditional methods are applied to partially overcome these problems and increase the effectiveness of diagnostic control in working conditions. The development of computer technology and its application in technology paved the way for the creation of more modern diagnostic monitoring systems. Modern diagnostic monitoring methods based on Soft Computing play an important role in optimizing the working condition of motors and increasing their stability. As a result of the research, it was found that the application of artificial neural networks and fuzzy logic-based diagnostic systems, which are pioneers of these methods, together with traditional methods in monitoring the technical condition of motors, will lead to the creation of new hybrid and complex systems. Keywords: Traction motor, fuzzy logic, neural networks, diagnostic monitoring.