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.
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