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 - the book addresses key challenges such as limited fault samples, class imbalance, distribution shifts, and data privacy. Other sections explore 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 that are provided to validate the effectiveness and generalizability of proposed methods
- Includes a novel data-knowledge hybrid driven framework that combines quantitative results with qualitative knowledge, enabling interpretable and explainable diagnostic outcomes
- 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
