Approaches and algorithms for processing experimental data and data obtained as a result of using modern means of measuring equipment, selecting diagnostic parameters, pattern recognition, which constitute the methodological basis for developing methods and designing tools for creating a service system for complex industrial facilities based on predicting their performance and residual life are described in submitted article. Along with classical methods, methods based on using the full potential of the modern elemental base of microprocessor technology and the use of artificial neural networks, machine learning, and "big data" are discovered. The given examples can serve as the basis for constructing a methodology for the application of the considered approaches for organizing predictive maintenance of complex industrial equipment. An analytical review of a number of scientific publications showed that the creation of new automated diagnostic systems that can increase fault tolerance and extend the life of sophisticated modern power equipment is extremely relevant. For this, various approaches are applied, based on mathematical models, expert systems, artificial neural networks and other algorithms. Summarizing the results of scientific publications, it can be argued that the implementation of a systematic approach to the organization of repair service at the enterprise requires a comprehensive solution to the following urgent problems: • monitoring is formulated as the task of interrogating sensors and collecting information necessary for further analysis; • diagnostics, it is solved as tasks of identifying informative signs with further detection and classification of failures and anomalies in data sets; • improving the accuracy of algorithms aimed at pattern recognition; • condition forecasting is the task of assessing the current and accumulated readings of monitoring systems for making decisions regarding either a specific element of the complex or the facilities. Thus, modern technology make it possible to arrange arbitrarily complex algorithms. However, to use the full potential that artificial neural networks, expert systems, and classical methods for identifying and diagnosing equipment it is necessary to have a conceptual development of the foundations of building systems for organizing maintenance and repair of complex energy equipment
NEURAL NETWORKS IN EXPERIMENTAL HIGH-ENERGY PHYSICS