Data-Efficient Intelligent Fault Detection and Diagnosis for Unmanned Aerial Vehicles
- 1st Edition - January 1, 2027
- Latest edition
- Author: Chuanjiang Li
- Language: English
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… Read more
Description
Description
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.
Key features
Key features
- 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
Readership
Readership
(1) Researchers and graduate students in the fields of UAV systems, fault diagnosis, intelligent maintenance, and data-driven modeling; (2) professionals and engineers working in aerospace, robotics, and autonomous systems, particularly those focusing on flight control, system health monitoring, and predictive maintenance; (3) AI and machine learning practitioners interested in applying data-efficient and small-sample learning methods to safety-critical applications in aeronautics; (4) developers and system integrators of UAV platforms seeking reliable fault detection strategies for real-world deployment
Table of contents
Table of contents
1: Introduction and Background
1.1 Low-Altitude Economy and UAV Applications
1.2 UAV System
1.2.1 Overview of UAV System
1.2.2 Sensor Subsystem
1.2.3 Flight Control Computer
1.2.4 Actuator Subsystem
1.3 Faults in UAV Flight Control Systems
1.3.1 Fault Classification
1.3.2 Analysis of Critical Failures References
2: Fundamentals of Intelligent FDD and Data-Efficient Learning
2.1 Overview of Intelligent Fault Detection and Diagnosis (FDD)
2.1.1 Definitions of Fault Detection and Fault Diagnosis
2.1.2 General Processes for FDD
2.2 Small Data Challenges in UAV FDD Tasks
2.2.1 Definition of Small Data
2.2.2 Causes of Small Data Problems
2.2.3 Impacts of Small Data on FDD Tasks
2.3 Key Data-Efficient Learning Techniques
2.3.1 Data Augmentation Methods
2.3.2 Meta-Learning Methods
2.3.3 Transfer Learning
2.3.4 Federated Learning References
3: Fault Detection for UAV Sensors based on CVAE-GAN Model under Zero-shot Conditions
3.1 Motivation
3.2 Related Works
3.3 Methodology
3.3.1 Problem Statement
3.3.2 CVAE-GAN Model Architecture
3.3.3 Loss Function
3.3.4 Reconstruction-Based Fault Detection
3.3.5 General Steps for Task Performing
3.4 Case Study
3.4.1 Dataset and Experimental Settings
3.4.2 Analysis of Data Reconstruction Performance
3.4.3 Analysis of Detection Performance for Different Faults
3.5 Conclusions References
4: Metric-Based Fault Diagnosis for UAV Actuators under Few and Imbalanced Data Scenarios
4.1 Motivation
4.2 Related Works
4.3 Methodology
4.3.1 Problem Definition
4.3.2 Siamese Hybrid Neural Network
4.3.3 Hybrid Spatial-Temporal Feature Extraction
4.3.4 Weighted Loss and Fine-Tuning Mechanism
4.3.5 General Steps for Task Performing
4.4 Case Study
4.4.1 Diagnosis Performance with Different Sample Sizes
4.4.2 Diagnosis Performance with Imbalanced Data
4.5 Conclusions References
5: Generalized Fault Diagnosis based on Meta-Learning for UAV Servo Bearings under Cross-Domain Scenarios
5.1 Motivation
5.2 Related Works
5.3 Methodology
5.3.1 Time-Frequency Signal Encoding
5.3.2 Meta-Task Construction
5.3.3 Backbone Architecture
5.3.4 Meta-Optimization Process
5.4 Case Study
5.4.1 Dataset and Experimental Settings
5.4.2 Diagnostic Generalization Across Working Conditions
5.4.2 Diagnostic Generalization Across Different Bearings
5.5 Conclusions References
6: Transformer-Based Few-Shot Learning for Fault Diagnosis with Noisy Labels and Domain Shifts
6.1 Motivation
6.2 Related Works
6.3 Methodology
6.3.1 Problem Definition
6.3.2 Task Organization
6.3.3 Model Design
6.3.4 Noise-Robust Learning Strategies
6.4 Case Study
6.4.1 CWRU Bearing Fault Case
6.4.2 Industrial Equipment Fault Case
6.5 Conclusions References
7: Data Privacy-Preserving Fault Diagnosis Based on Personalized Federated Learning
7.1 Motivation
7.2 Related Works
7.3 Methodology
7.3.1 Adaptive Interpolation for Feature Extractors
7.3.2 Reconstruction for Classifiers
7.3.3 Personalized Federated Learning Process
7.4 Case Study
7.4.1 CWRU Bearing Fault Case
7.4.2 Industrial Equipment Fault Case
7.5 Conclusions References
8: Data-Knowledge Driven Intelligent FDD Framework for UAVs
8.1 Motivation
8.2 Fault Knowledge Extraction for UAV Subsystems
8.2.1 Fault Knowledge Extraction for Sensors
8.2.2 Fault Knowledge Extraction for Actuators
8.2.3 Fault Knowledge Extraction for Servo Motors
8.3 Construction of UAV FDD Ontology Knowledge Base
8.3.1 Knowledge Representation of UAV FDD based on OWL
8.3.2 Knowledge Reasoning of UAV FDD based on SWRL
8.4 Ontology Knowledge Base Construction
8.5 Intelligent FDD Framework based on Fusion of Data and Ontology Knowledge
8.4.1 Frame Structure
8.4.2 Semantic Mapping Method
8.6 Conclusions References
1.1 Low-Altitude Economy and UAV Applications
1.2 UAV System
1.2.1 Overview of UAV System
1.2.2 Sensor Subsystem
1.2.3 Flight Control Computer
1.2.4 Actuator Subsystem
1.3 Faults in UAV Flight Control Systems
1.3.1 Fault Classification
1.3.2 Analysis of Critical Failures References
2: Fundamentals of Intelligent FDD and Data-Efficient Learning
2.1 Overview of Intelligent Fault Detection and Diagnosis (FDD)
2.1.1 Definitions of Fault Detection and Fault Diagnosis
2.1.2 General Processes for FDD
2.2 Small Data Challenges in UAV FDD Tasks
2.2.1 Definition of Small Data
2.2.2 Causes of Small Data Problems
2.2.3 Impacts of Small Data on FDD Tasks
2.3 Key Data-Efficient Learning Techniques
2.3.1 Data Augmentation Methods
2.3.2 Meta-Learning Methods
2.3.3 Transfer Learning
2.3.4 Federated Learning References
3: Fault Detection for UAV Sensors based on CVAE-GAN Model under Zero-shot Conditions
3.1 Motivation
3.2 Related Works
3.3 Methodology
3.3.1 Problem Statement
3.3.2 CVAE-GAN Model Architecture
3.3.3 Loss Function
3.3.4 Reconstruction-Based Fault Detection
3.3.5 General Steps for Task Performing
3.4 Case Study
3.4.1 Dataset and Experimental Settings
3.4.2 Analysis of Data Reconstruction Performance
3.4.3 Analysis of Detection Performance for Different Faults
3.5 Conclusions References
4: Metric-Based Fault Diagnosis for UAV Actuators under Few and Imbalanced Data Scenarios
4.1 Motivation
4.2 Related Works
4.3 Methodology
4.3.1 Problem Definition
4.3.2 Siamese Hybrid Neural Network
4.3.3 Hybrid Spatial-Temporal Feature Extraction
4.3.4 Weighted Loss and Fine-Tuning Mechanism
4.3.5 General Steps for Task Performing
4.4 Case Study
4.4.1 Diagnosis Performance with Different Sample Sizes
4.4.2 Diagnosis Performance with Imbalanced Data
4.5 Conclusions References
5: Generalized Fault Diagnosis based on Meta-Learning for UAV Servo Bearings under Cross-Domain Scenarios
5.1 Motivation
5.2 Related Works
5.3 Methodology
5.3.1 Time-Frequency Signal Encoding
5.3.2 Meta-Task Construction
5.3.3 Backbone Architecture
5.3.4 Meta-Optimization Process
5.4 Case Study
5.4.1 Dataset and Experimental Settings
5.4.2 Diagnostic Generalization Across Working Conditions
5.4.2 Diagnostic Generalization Across Different Bearings
5.5 Conclusions References
6: Transformer-Based Few-Shot Learning for Fault Diagnosis with Noisy Labels and Domain Shifts
6.1 Motivation
6.2 Related Works
6.3 Methodology
6.3.1 Problem Definition
6.3.2 Task Organization
6.3.3 Model Design
6.3.4 Noise-Robust Learning Strategies
6.4 Case Study
6.4.1 CWRU Bearing Fault Case
6.4.2 Industrial Equipment Fault Case
6.5 Conclusions References
7: Data Privacy-Preserving Fault Diagnosis Based on Personalized Federated Learning
7.1 Motivation
7.2 Related Works
7.3 Methodology
7.3.1 Adaptive Interpolation for Feature Extractors
7.3.2 Reconstruction for Classifiers
7.3.3 Personalized Federated Learning Process
7.4 Case Study
7.4.1 CWRU Bearing Fault Case
7.4.2 Industrial Equipment Fault Case
7.5 Conclusions References
8: Data-Knowledge Driven Intelligent FDD Framework for UAVs
8.1 Motivation
8.2 Fault Knowledge Extraction for UAV Subsystems
8.2.1 Fault Knowledge Extraction for Sensors
8.2.2 Fault Knowledge Extraction for Actuators
8.2.3 Fault Knowledge Extraction for Servo Motors
8.3 Construction of UAV FDD Ontology Knowledge Base
8.3.1 Knowledge Representation of UAV FDD based on OWL
8.3.2 Knowledge Reasoning of UAV FDD based on SWRL
8.4 Ontology Knowledge Base Construction
8.5 Intelligent FDD Framework based on Fusion of Data and Ontology Knowledge
8.4.1 Frame Structure
8.4.2 Semantic Mapping Method
8.6 Conclusions References
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 1, 2027
- Language: English
About the author
About the author
CL
Chuanjiang Li
Chuanjiang Li is an Associate Professor at Guizhou University. His research focuses on unmanned aerial vehicles, big data, artificial intelligence, and intelligent maintenance systems. He serves on the committees of the Industrial Big Data and Intelligent Systems Branch of the Chinese Mechanical Engineering Society and the Unmanned Systems Branch of the Chinese Command and Control Society. He has led or contributed to several national-level projects, including the National Key R&D Program and the National Natural Science Foundation of China
Affiliations and expertise
Guizhou University, China