Research Progress on the Reliability of Aircraft Emergency Hatch Ejection Drive Mechanisms Integrated with Artificial Intelligence

Artificial intelligence (AI) and advanced sensor technologies have developed into maintenance, reliability, and optimized aircraft emergency hatch ejection drive mechanisms. The review study narrates research on AI-driven approaches: machine learning, deep learning, federated learning, and generative algorithms (GAs) for fault diagnosis. Internet of Things (IoT) enabled real-time monitoring and intelligence maintenance techniques. Recurrent neural networks particularly long short-term memory (LSTM) models and ML techniques analyse the sensor data for prediction of stress, wear, and failure modes of components, achieving high accuracy and enabling proactive interventions. The results reveal that IoT networks coupled with AI approaches continuously monitor critical parameters including vibration and temperature. This study reveals that optimization techniques, genetic algorithms, and particle swarm optimization play significant roles in enhancing the structural design of components like gears and connecting rods that achieve weight reductions without compromising safety. This study identifies challenges of model interpretability, data scarcity, and regulatory compliance. These issues are addressed by proposing hybrid AI models and federated learning. Emerging trends include self-healing materials, quantum ML approaches, and automated maintenance systems, which could transform safety in the aviation industry. By bridging research gaps between engineering practices and data-driven insights, this study shows that AI can play a vital role in advancing the efficiency and reliability of drive mechanisms in next-generation aircraft, compliance with aviation protocols, and paving the way for adaptive and autonomous systems.