Abstract Details
218 / 2025-06-14 21:58:18
Meta-Learning-Based Fault Diagnosis Method for Multi-Rotor Unmanned Aerial Vehicles
Fault diagnosis,Multi-rotor UAV,RflyMAD,Meta-learning,Few-shot learning
Abstract Accepted
Addressing the critical challenges of scarce fault samples in multi-rotor UAVs operating under complex conditions and diagnostic difficulties from multi-source sensor fusion, this study develops a meta-learning-based framework for few-shot fault diagnosis. Utilizing the RflyMAD dataset from Beihang University's Reliable Flight Control Group, we systematically analyze sensor and actuator fault characteristics. A hierarchical meta-feature extraction network is proposed, integrating temporal feature learning and cross-sensor attention mechanisms. The framework implements three meta-learning approaches: Model-Agnostic Meta-Learning (MAML) for parameter adaptation, Memory-Augmented Neural Networks (MANN) for pattern retrieval, and Prototypical Networks (ProtoNet) for metric-based classification. This methodology provides a novel pathway for handling data-scarce fault diagnosis scenarios, demonstrating the potential to improve diagnostic efficiency while reducing dependency on large-scale fault data.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
