Multi-Channel Multi-Scale and Graph Relationship Meta Learning Intelligent Detection Method in Aircraft Engine Fault Maintenance

Abstract Due to the complexity of the operating environment and the diversity of components, fault maintenance and detection of aircraft engines have always been a huge challenge. To improve the reliability and maintenance efficiency of aircraft engines, a multi-channel, multi-scale, and graph relational meta learning intelligent detection method is proposed. By combining multi-channel data fusion, multi-scale feature extraction, graph neural networks, and meta learning strategies, a comprehensive understanding of fault characteristics and their propagation relationships is achieved. In the research results, the proposed multi-channel, multi-scale, and graph relation meta learning intelligent detection method has better performance, with a localization accuracy of 0.94 and an average localization error of 0.11. Meanwhile, its training time is 15.2 min, and inference time is 49.65 milliseconds, demonstrating high computational efficiency. In addition, the multi-channel multi-scale and graph relation meta learning intelligent detection method has shown good robustness under different testing conditions. The results show that this method not only has significant advantages in the accuracy and efficiency of fault detection, but also has excellent fault localization performance, significantly reducing the safety risks and economic losses caused by aircraft engine failures. The research provides a theoretical basis for real-time fault monitoring and maintenance decision-making of aircraft engines, which is beneficial for improving the efficiency and quality of maintenance work.