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Article information
2020 , Volume 25, ¹ 6, p.95-103
Osipov A.V., Timoshenko A.V., Perlov A.Y., Lvov K.V.
Intelligent system for supporting functional characteristics of radar monitoring based on failure prediction
Purpose. This work is devoted to a description of an intelligent support system (ICP) of the functional characteristics of radar systems operating in heat-stressed modes. The purpose of the work is to simulate the operation of the failure prediction module based on the data on the operation of the critical components of the radar. Methodology. Failure prediction based on block temperature data was performed using supervised machine learning methods, including regression methods. The highest quality parameters of the forecasting model (horizon — accuracy) were achieved when training the model using the gradient boosting method, which allows organizing continuous additional training of the forecasting model during the operation of the radar by forming a training sample consisting of a data flow about the technical state. Findings. A structural and functional diagram of the ICP has been developed and a description of the operation of its components is given. The error (20%) and the horizon (20 min.) of the prediction of failures obtained during the experiments are sufficient for the staff serving the radar to take the necessary measures and prevent a critical decrease in the functional characteristics of the product. Originality/value. The introduction of ISP will allow realizing the maximum functional capabilities of the radar, taking into account the forecast of their change, as well as making an operational report to the command post of the radar on the reasons for the decrease in functional readiness according to the pop-up tips about the faulty element and the degree of its influence on the functional characteristics.
[full text] [link to elibrary.ru]
Keywords: RLC, artificial intelligence, forecasting, energy potential, radiated power, machine learning
doi: 10.25743/ICT.2020.25.6.006
Author(s):
Osipov Alexandr Vladimirovich
Position: General director
Office: Joint-stockcompany Natsional'nyy Ekologicheskiy operator
Address: 125047, Russia, Moscow
E-mail: a.osipov@rt-neo.ru
Timoshenko Alexander Vasilyevich
Dr. , Professor
Position: Head of Laboratory
Office: Joint-stock company Academician A.L. Mintz Radiotechnical institute
Address: 127083, Russia, Moscow
E-mail: u567ku78@gmail.ru
SPIN-code: 7172-8764Perlov Anatoly Yuryevich
PhD.
Position: Head of department
Office: Joint-stockcompany Academician A. L. Mintz Radiotechnical institute
Address: 127083, Russia, Moscow
E-mail: laperlov@yandex.ru
SPIN-code: 9215-1124Lvov Kirill Vyacheslavovich
Position: Student
Office: M.V. Lomonosov Moscow state university, Joint-stockcompany Academician A.L. Mintz Radiotechnical institute
Address: 119234, Russia, Moscow
E-mail: lvov.kv14@physics.msu.ru
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Bibliography link:
Osipov A.V., Timoshenko A.V., Perlov A.Y., Lvov K.V. Intelligent system for supporting functional characteristics of radar monitoring based on failure prediction // Computational technologies. 2020. V. 25. ¹ 6. P. 95-103
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