Постановка наукового завдання удосконалення методів оцінки надійності БПЛА на всіх етапах життєвого циклу

Abstract

This article provides a formal statement of the scientific task directed at modernizing reliability assessment methodologies for Unmanned Aerial Vehicles (UAVs). The author identifies a critical gap in traditional aviation reliability theories, which often neglect the cyber-physical complexity and software-dependent nature of autonomous systems. The proposed framework shifts from discrete, stage-based evaluations to a continuous, integral reliability function $R_{total}$ that accounts for hardware, software, communication links, and human operator factors throughout the entire life cycle. By integrating advanced complex information processing (CIP) with deep learning algorithms, the study establishes a foundation for detecting latent pre-failure states within high-dimensional telemetry streams.The research explores the efficacy of multi-sensor data fusion, particularly leveraging Kalman filtering techniques to mitigate the impact of anomalous navigation measurements. A significant focus is placed on transitioning from reactive maintenance to pre dictive health monitoring by leveraging Machine Learning (ML) techniques and CIP algorithms. The methodology introduces neural networks and classification algorithms to identify hidden failure patterns and incipient anomalies within large telemetry datasets, which traditional analytic models often overlook. Furthermore, the paper evaluates the impact of environmental uncertainty on airworthiness during dynamic mission phases. Ultimately, solving this formulated multi-criteria optimization task provides a robust mathematical and algorithmic foundation for real-time digital reliability monitoring. This approach ensures that highly autonomous UAV operations can be safely and systematically integrated into the Safety Management Systems (SMS) and airworthiness standards regulated by ICAO.