45 / 2025-05-13 14:00:56
A UAV rudder fault diagnosis method under cross-flight conditions
UAV, fault diagnosis, transfer learning, MMD
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Yizong Zhang / Guizhou University;School of Mechanical Engineering
Shaobo Li / Guizhou University;Guizhou Institute of Technology
Yanying Gu / Anshun University
Xue An / Guizhou University
Zihao Liao / Guizhou University
Although deep learning has become a mainstream method for Unmanned Aerial Vehicle (UAV) fault diagnosis by virtue of its powerful feature learning capability, most of the existing diagnostic models suffer from insufficient environmental adaptability. Therefore, this paper proposes an Unsupervised Transfer Learning-based Feature Mapping (UTL-FM) for UAV rudder fault diagnosis (UTL-FM), which constructs a cross-domain knowledge transfer framework to efficiently transfer the diagnostic models learnt from the source domain to unknown flight scenarios by deeply mining the fault features and diagnostic knowledge in the known flight environment. The method firstly aligns the source and target domain samples based on the feature space mapping to ensure that the data of the two domains present uniform distribution characteristics in the feature space. Secondly, a minimization process is implemented on the feature distributions of the source and target domains using the Maximum Mean Difference (MMD) metric criterion to minimize the domain variances. Finally, extensive experiments were conducted in real flight cases to verify the superiority of UTL-FM.
重要日期
  • 会议日期

    08月01日

    2025

    08月04日

    2025

  • 06月15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
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