Method of developing automated information support for integrated modular avionics systems: monitoring, diagnostics and reliability improvement
Year: 2025
Type: article
Abstract: Advances in avionics technology have significantly increased the complexity of modern aircraft systems. Integrated modular avionics (IMA) plays a critical role in providing an efficient, reliable, and maintainable avionics architecture by consolidating multiple functions onto common computing resources. This paper presents a method for developing automated information support software for IMA, focusing on real-time monitoring, diagnostics, and state management of avionics modules. The proposed approach emphasizes automation of data collection and processing, which enables early fault detection, predictive maintenance, and rapid response to component failures or degradation. By integrating automated monitoring capabilities, aircraft operators can improve system reliability, minimize downtime, and optimize maintenance planning. This paper highlights key cybersecurity considerations, including protection from external threats, intrusion detection, and secure data transfer between IMA modules. As networked avionics systems become increasingly prevalent, implementing robust security measures is essential to prevent unauthorized access, data manipulation, or system failures that could jeopardize flight safety. Additionally, this study examines key aspects of system architecture design, emphasizing modularity, scalability, and compliance with industry standards such as ARINC 653 and DO-178C. The modular nature of IMA provides greater flexibility in system design, allowing new features to be integrated or existing modules to be upgraded without extensive reconfiguration. Compliance with aviation safety standards and software certification ensures that automated information support solutions meet regulatory requirements. Recommendations for improving diagnostic and monitoring algorithms are provided to optimize system performance. Advanced data analytics techniques, including machine learning and predictive analytics, are explored to improve fault detection accuracy and optimize maintenance schedules. Using real-time data analytics, operators can implement condition-based maintenance strategies, reducing unnecessary inspections while improving overall system reliability. The results of this study confirm that the integration of automated information support systems significantly improves the efficiency and reliability of IMA-based avionics. By implementing advanced monitoring and diagnostic capabilities, these systems contribute to increased aircraft availability, reduced maintenance costs, and improved flight safety. In addition, by ensuring compliance with modern aviation standards, such solutions align with industry best practices and regulatory requirements. In conclusion, the development and implementation of automated information support software for IMA represents an important step towards achieving higher levels of operational efficiency, reliability, and cybersecurity in modern aviation. As avionics systems continue to evolve, the integration of intelligent monitoring solutions will be essential to meet the growing demands of next-generation aircraft while ensuring compliance with stringent safety and certification standards.
Authors: Y.B. Kovalenko, I.O. Kozlyuk
Institution: National University "Kyiv Aviation Institute"
Language: English
Topic: Engineering and Test Systems
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409038179