Chuanjiang Li – författare

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 tasksIncludes practical case studies using real UAV flight data and industrial fault experiments that are provided to validate the effectiveness and generalizability of proposed methodsIncludes a novel data-knowledge hybrid driven framework that combines quantitative results with qualitative knowledge, enabling interpretable and explainable diagnostic outcomesExplores 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