报告详情
Meta-Learning-Based Fault Diagnosis Method for Multi-Rotor Unmanned Aerial Vehicles
编号：68
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更新：2025-06-20 16:40:35 浏览：18次
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报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
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.
关键词
Fault diagnosis,Multi-rotor UAV,RflyMAD,Meta-learning,Few-shot learning
报告人
Can Guo
Ph.D. student in pro National University of Defense Technology稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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