ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 9. Vol. 28. 2022

DOI: 10.17587/it.28.475-484

G. S. Veresnikov, Dr. Tech. Sc., Leading Researcher, A. V. Golev, Junior Researcher, A. M. Moskovtsev, Engineer,
M. P. Martirosyan, Engineer,
 Institute of Control Sciences V. A. Trapeznikov Academy of Sciences Senior Researcher,
Moscow, 117997, Russian Federation

Methods and Algorithms for Solving the Problem of Early Diagnostics of Technical Objects Using Data Mining Methods

We are seeing a huge increase in the productivity of technical facilities. But as soon as the object is used for its intended purpose, its technical resource begins to be consumed. Early diagnostics and condition detection are important for reliable and safe operation of technical facilities. The results of early diagnostics are used both for prevention of emergency situations and for operation of technical objects according to their condition. In this article, the task is to analyze the methods and algorithms of data mining used in the tasks of determining and predicting the state of technical objects. Neural networks, decision trees, growing pyramid networks, metric algorithms, and the support vector method are considered. Advantages and disadvantages of data mining methods and algorithms are given. Special attention is paid to algorithms and methods for identifying informative features in empirical data, in particular, filtering, projection, and wrapping methods. The use of these methods and algorithms is important to simplify the models used to classify the States of technical objects and improve the accuracy of diagnostics. Analysis of the world scientific literature has shown the relevance of using methods and algorithms for data mining in integration with methods for identifying informative features. These methods are usually used in conjunction with wrapper methods and filters to improve diagnostic accuracy and condition detection of technical objects.
Keywords: diagnostics, intelligent analysis, neural networks, informative features, technical objects

P. 465–474

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