Кваліфікаційні роботи здобувачів вищої освіти кафедри авіаційних комп'ютерно-інтегрованих комплексів (НОВА)

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An intelligent system for predicting the need to replace aircraft parts based on transformers
(National Aviation University, 2024-06-15) Dziuba, Andrii Olegovych; Дзюба, Андрій Олегович
This study centers on crafting and implementing an intelligent framework for anticipating aviation part replacements through transformer models. Transformers, a sophisticated class of deep learning models celebrated for their exceptional performance in natural language processing, have recently demonstrated significant potential in diverse predictive assignments. Leveraging transformers, this study strives to establish an advanced predictive maintenance system adept at precisely forecasting optimal part replacement timings. The proposed system is tailored to scrutinize extensive operational and sensor data from aircraft, identifying pre-failure patterns and irregularities. By foreseeing part replacement probabilities and schedules, the system can aid airlines and maintenance crews in refining their maintenance calendars, curtailing downtime, and curbing expenses. Furthermore, this methodology aligns with the industry's progression towards predictive maintenance, emphasizing data-centric decision-making to enhance operational efficiency and safety.