Интеллектуальная система поддержки функциональных характеристик РЛК мониторинга на основе прогнозирования отказов

Abstract

Показаны особенности применения методов искусственного интеллекта в современных радиолокационных комплексах (РЛК) мониторинга для организации превентивного технического обслуживания. Приведена структурно-функциональная схема интеллектуальной системы поддержки функциональных характеристик РЛК. Представлены экспериментальные оценки точности прогнозирования излучаемой мощности РЛК по данным диагностического контроля. 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.