Research on fault detection method of aerial sensor based on neural network

This research addresses the critical need for reliable sensor systems in the aviation industry and the challenges associated with timely and accurate fault detection. We propose a novel method for enhancing fault detection in aviation sensors using <i>advanced</i> neural network architectures. Our methodology involves a comprehensive exploration of neural network models tailored to the characteristics of aviation sensor data. Experiments, conducted with a meticulously designed dataset, quantitatively evaluate the proposed approach using established performance metrics. The results unequivocally demonstrate the effectiveness of our neural network-based method in detecting and diagnosing sensor faults, showcasing its potential to significantly improve the reliability of aviation systems. In conclusion, this study contributes to aviation safety by presenting an innovative method for sensor fault detection, underscoring the applicability and effectiveness of neural networks in addressing challenges associated with sensor reliability. Future prospects involve refining the model, exploring real-time implementation possibilities, and extending the methodology to diverse sensor types, providing valuable insights for advancing the state-of-the-art in aviation sensor fault detection and enhancing the safety and performance of aircraft systems.