V.V. Lisitsky1, D.M. Vyaldin2

1,2 Military Space Academy named after A.F. Mozhaisky (Saint-Petersburg, Russia)

1 vka@mil.ru, 2 dmitriy_vyaldin@mail.ru

An analysis of the operation of the monitoring and diagnostics system of information and measurement systems has shown that data from all elements of the equipment connected to each other, as a rule, according to a hierarchical principle based on constructive division, are used to obtain information about a critical change in the values of technical characteristics. When solving the problem of forecasting technical characteristics in the interests of justifying the volume of maintenance, a large number of elements leads to a decrease in the accuracy and horizon of the forecast due to the presence of excessive information about elements that do not affect the deviation of technical characteristics from the nominal value. To ensure the necessary accuracy of the information and measurement system, it is necessary to use a large array of data on the technical characteristics of structural elements with their subsequent processing using machine learning models. To consider a methodology based on forecasting the values of the technical characteristics of the elements of an information and measurement system to determine their critical importance. Results. To solve the problem of predicting the values of the technical characteristics of an information and measurement system, it is proposed to use a gradient boosting algorithm and a linear regression method. The proposed method makes it possible to organize continuous training of the algorithm for determining critical elements in real time at the operational stage of the information and measurement system.

Lisitsky V.V., Vyaldin D.M. Methodology for determining critical elements of an information and measurement system based on forecasting the values of their technical characteristics. Neurocomputers. 2024. V. 26. № 2. Р. 70-78. DOI: https://doi.org/10.18127/ j19998554-202402-07 (In Russian)

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