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The present paper considers a multi-criteria analysis algorithm of turbo generator fluidfilm bearing operability and its connection with rotor machine monitoring system data. It is substantiated that implementation of predictive analysis of load capacity, locus curves and dynamic displacements allows prognosis of useful life of a fluid-film bearings and improvement of reliability of a rotor machine.

The article considers general approaches and modern monitoring systems for rotary machines of electric generating equipment. The main characteristics of monitoring and diagnostics systems of Russian and foreign manufacturers are presented. Modern trends in the construction of intelligent systems for analyzing the performance of turbo generators and predicting possible failures in order to minimize the cost of repairs and forced shutdown of equipment are outlined. The concept of adaptive-predictive use of rotary machines, the difference from existing systems is the presence of adaptive module that allows to react to unwanted changes in real time and increase the predicted residual resource or eliminate the predicted probability of initially refusal.

The article describes general approaches to creating an intelligent system for monitoring and diagnosing the operability of energy supply facilities. The general concept of the adaptive-predictive analysis system and the construction of an artificial neural network for its use in the predictive module for predicting the type and time of failure occurrence is given. The basic principles of training a neural network for recognizing various types of failures are also given. Critical remarks of the concept of creating a digital twin of such a complex object for modeling as energy-generating equipment are given.

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