| Найдено документов - 2 | Статьи из номера журнала: RUSSIAN AERONAUTICS. T. 68, № 3. - USA : Allerton Press, 2025. - Текст : электронный. | Версия для печати |
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1. Статья из журнала
| Assessment of technical condition of radar station elements based on optimization of built-in diagnostic monitoring tools polling frequency / A. Yu. Perlov, V. A. Pankratov, S. V. Matseevich, K. V. L'vov. - Текст : электронный // RUSSIAN AERONAUTICS. - USA : Allerton Press, 2025. - T. 68, № 3. - pp. 770-771. - URL: https://link.springer.com/article/10.3103/S1068799825030262 (дата обращения: 13.05.2026). - Режим доступа: национальная подписка. - URL: https://elibrary.ru/item.asp?id=89065272 (дата обращения: 13.05.2026). | |
| Авторы: | Perlov, A. Yu., Pankratov, V. A., Matseevich, S. V., L'vov, K. V. |
| Ключевые слова: | Radar station, Diagnostic and monitoring system, Sensor polling frequency, Technical condition forecasting |
| Аннотация: | A methodology for assessing the technical condition of radar station elements was developed through optimization of the polling frequency for built-in diagnostic monitoring tools. The proposed methodology reduces the decision-making time for radar station operators in maintaining functional characteristics at specified levels by enabling a real-time continuous assessment and prediction of technical condition of element |
| Поиск: | Источник |
| Ссылка на ресурс: | https://elibrary.ru/item.asp?id=89065272 |
| Ссылка на ресурс: | https://link.springer.com/article/10.3103/S1068799825030262 |
| Электронный документ | Для просмотра необходимо войти в личный кабинет |
2. Статья из журнала
| Algorithm for diagnostics of an aircraft electromechanical drive using a fully connected neural network of the autoencoder type / G. S. Veresnikov, G. M. Avkhimenko, A. V. Skryabin [и др.]. - Текст : электронный // RUSSIAN AERONAUTICS. - USA : Allerton Press, 2025. - T. 68, № 3. - pp. 763-769. - URL: https://link.springer.com/article/10.3103/S1068799825030262 (дата обращения: 13.05.2026). - Режим доступа: национальная подписка. - URL: https://elibrary.ru/item.asp?id=89065271 (дата обращения: 13.05.2026). | |
| Авторы: | Veresnikov, G. S., Avkhimenko, G. M., Skryabin, A. V., Goncharenko, V. I., Mikhailov, Yu. G., Sobolev, D. N. |
| Ключевые слова: | Anomaly detection, Autoencoder, Electromechanical actuator, Technical condition diagnostics, Neural network |
| Аннотация: | This paper proposes an algorithm for analyzing the technical condition of an electromechanical actuator deflecting the control surface of an unmanned aerial vehicle using machine learning models designed for anomaly detection. These models address the well-known novelty detection problem in observed data, which is interpreted as a fault in the monitored system. Computational experiments were conducted using data generated by simulating the actuator operation. Faults were considered. The procedure for splitting data into training and test sets is described. A fully connected neural network with an autoencoder architecture was selected to solve the anomaly detection problem. The paper discusses the extraction of diagnostic features and presents a methodology for deriving them from time series. The efficiency of the proposed diagnostic algorithm is estimated on various test sets, and the results are analyzed |
| Поиск: | Источник |
| Ссылка на ресурс: | https://elibrary.ru/item.asp?id=89065271 |
| Ссылка на ресурс: | https://link.springer.com/article/10.3103/S1068799825030262 |
| Электронный документ | Для просмотра необходимо войти в личный кабинет |