Reliability and Safety Analysis of Intelligent Flight Control Systems for Civil Aircraft

Abstract

Civil aviation is moving toward intelligent flight control. This shift improves automation and operational efficiency. However, the use of intelligent systems also introduces new challenges for reliability and safety. This study investigates key reliability measures for intelligent flight systems. These include rates of in-flight interruptions and delays. It also examines the maintenance technologies that support these systems. One important trend is the move to data-driven predictive maintenance. This approach uses big data and machine learning. Training for maintenance technicians is also a key factor for overall system reliability. The study also looks at risks from cybersecurity threats. Examples include ADS-B spoofing attacks. Results show that intelligent flight systems in civil aircraft need to combine advanced methods. These methods include reliability and safety co-design, explainable AI (XAI), and digital twin verification platforms. Together, they help create a defense with multiple layers. A key part of this is strict network separation between critical and non-critical areas. Looking ahead, integrated frameworks and cooperation across different fields will be important. This will support further progress in intelligent flight systems toward improved reliability and safety.