Technical Diagnostics of Industrial Equipment Based on Soft Computing Technologies

This paper proposes technical diagnostics methods of industrial equipment on the example of asynchronous electric motors using soft computing technologies. The proposed methods can be used in decision support systems for diagnosing industrial equipment. The working principle of the selective method for preliminary processing of diagnostic data is considered in detail, which carries out specialized processing based on the automatic selection of a data processor for each specific mathematical method. The preprocessing is based on an event system, which is a set of state machines. Also, the paper proposes a method for diagnosing industrial equipment, based on finding the diagnostic parameters values and factors affecting the equipment operation in specified ranges using soft computing technologies. A numerical example of diagnosing an asynchronous electric motor of an oil industry enterprise is considered with accounting the main diagnostic parameters (power, sliding, bearing temperature and others) that characterize the engine; power quality indicators and diagnostic factors (for example, information about climatic conditions) that affect the basic parameters of the engine.