Data-Efficient Intelligent Fault Detection and Diagnosis for Unmanned Aerial Vehicles

As low-altitude economy initiatives accelerate the deployment of UAVs in logistics, inspection, and emergency response, the need for safe and autonomous UAV operation has become increasingly critical. Data-Efficient Intelligent Fault Detection and Diagnosis for Unmanned Aerial Vehicles presents a comprehensive approach to intelligent fault detection and diagnosis in UAV systems under data-scarce and complex flying conditions. Focusing on the flight control system - the core of UAV autonomy - it addresses key challenges such as limited fault samples, class imbalance, distribution shifts, and data privacy. The book explores data-efficient learning techniques, including generative adversarial models, meta-learning, and federated learning to enable accurate and robust diagnosis of sensor, actuator, and control surface faults. Additionally, it introduces a data-knowledge hybrid driven framework that maps quantitative results to a structured fault ontology, enhancing interpretability and maintenance efficiency. By combining theory with real-world cases, this book provides researchers, engineers, and graduate students with practical tools and insights for developing reliable and intelligent UAV health monitoring systems to ensure the safety of low-altitude economy.

• Presents advanced learning techniques tailored for small data and domain bias scenarios, addressing the data scarcity challenges common in UAV fault diagnosis tasks
• Includes practical case studies using real UAV flight data and industrial fault experiments are provided to validate the effectiveness and generalizability of the proposed methods
• A novel data-knowledge hybrid driven framework is included, which combines quantitative results with qualitative knowledge, enabling interpretable and explainable diagnostic outcomes
• A dedicated chapter explores a personalized federated meta-learning approach for UAV fault diagnosis, enabling collaborative learning across distributed UAV systems while protecting sensitive flight data and supporting decentralized deployment in real-world applications