Abstract. This research explores the creation of a novel training complex that includes an engineering support system specifically designed for the technical management of military and specialized aviation equipment. The importance of this study stems from the need to improve the training efficiency of engineering and technical staff, given the growing technical complexity of aviation equipment and the increasing demands for aviation safety. The main aim of this research is to develop an adaptive educational platform that integrates digital twins of aviation systems, fault diagnosis algorithms, and intelligent methods to tailor the educational process. Within this study, the architecture of a comprehensive engineering support system was devised, which includes data collection, digital modeling, and analytics. An adaptive learning algorithm has been introduced, which takes into account the trainee’s skill level, experience, and progress in mastering the material, thereby automatically adjusting the curriculum. The use of virtual simulators and simulation models enabled the development of a flexible training system that closely mirrors the actual operating conditions of aviation equipment. To evaluate the proposed system’s effectiveness, experimental testing was carried out, comparing the training results of two groups of engineering and technical personnel: the experimental group (using adaptive training with digital models) and the control group (using traditional training methods). The findings showed that the new training complex reduced response time to emergencies by 22%, lowered the number of errors by 30%, and improved the accuracy of procedure execution by 18%. The adaptive training system developed is highly flexible, allowing for customization and integration with modern predictive diagnostics methods for aircraft malfunctions. Implementing such a complex in the training of specialists will enhance aviation safety, reliability, and the economic efficiency of technical aircraft operations.
Keywords: aviation equipment, engineering support, digital twins, adaptive learning, training complex, fault diagnostics.
