Advanced radiation resistant packaging technology and reliability prediction model for avionics systems

In the context of intelligent manufacturing, big data-driven device fault prediction and health management (PHM) is receiving widespread attention. Deep learning (DL) has become a research hotspot due to its powerful ability in hierarchical feature extraction, which can uncover deep level knowledge hidden in data and has good domain adaptability. Avionics systems operate in complex and ever-changing radiation environments, often influenced by high-energy particle flows such as solar activity, cosmic rays, and the Earth's radiation belt. These radiations can cause damage to electronic components, leading to a decrease in system performance or even failure. To extend the service life of aviation electronic components, radiation resistant packaging technology is widely adopted. This article focuses on advanced radiation resistant packaging technology for avionics systems and proposes a reliability prediction model based on DL. This model utilizes the powerful data processing capabilities of DL to analyze the operational data of aviation electronic devices, achieving accurate fault prediction and PHM. The results show that our model performs well in fault prediction accuracy and efficiency, significantly improving the reliability and safety of avionics systems.