Samara University Repository
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This is an open electronic archive created to preserve and disseminate the scientific results of our university. The works of researchers, faculty members, and students are available to everyone for academic and research purposes.
The repository contains dissertations, research articles, educational and methodological materials, monographs, and regulatory documents. The collections cover a wide range of fields: from aerospace technologies, engine engineering, and computer science to history, linguistics, mathematics, and ecology. The archive brings together the scientific heritage of the university, including materials prepared during the period of Samara State Aerospace University and Samara State University.
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| Title: | Подход к прогнозированию остаточного ресурса авиационных двигателей |
| Authors: | Абдулнагимов А. И. Алекторов Г. К. |
| Keywords: | aircraft power plants C-MAPSS dataset forecasting methods gas turbine engines intelligent algorithms LSTM models LSTM-модели neural networks parameter analysis residual life assessment Transformer авиационные силовые установки анализ параметров газотурбинные двигатели интеллектуальные алгоритмы методы прогнозирования набор данных C-MAPSS нейронные сети оценка остаточного ресурса |
| Issue Date: | 2025 |
| Citation: | Абдулнагимов, А. И. Подход к прогнозированию остаточного ресурса авиационных двигателей = Approach to predicting remaining useful life of aircraft engines / А. И. Абдулнагимов, Г. К. Алекторов // Перспективы развития двигателестроения : материалы междунар. науч.-техн. конф. им. Н. Д. Кузнецова (18–20 июня 2025 г.) / Самар. нац. исслед. ун-т им. С. П. Королева (Самар. ун-т) ; редкол.: Е. В. Шахматов, А. И. Ермаков. - Самара : Изд-во Самар. ун-та, 2025. - С. 315-318. |
| Abstract: | The study explores intelligent models based on LSTM recurrent neural networks and the Transformer architecture for predicting degradation and remaining useful life of aircraft power plants. The C-MAPSS dataset is used for simulating the operation of a turbo fan jet engines. The proposed Transformer model with a modified encoder block demonstrates good prediction accuracy and generalization capability compared to LSTM. |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/43311 |
| Appears in Collections: | Перспективы развития двигателестроения |
| File | Size | Format | |
|---|---|---|---|
| 978-5-7883-2191-2_2025-315-318.pdf | 424.04 kB | Adobe PDF | View/Open |
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