Fault Detection of FW-UAV Based on Spatiotemporal Feature Networks

In fixed-wing unmanned aerial vehicle (FWUAV), anomalous states can propagate errors internally due to the closed-loop nature of flight control systems. This complicates the differentiation of sensor signal variations caused by different component failures, hindering fault detection models from accurately capturing hidden fault features. As a result, FW-UAV may struggle to perceive and interpret critical flight parameters from onboard sensor data, affecting accurate flight state cognition. To address this, we propose a multi-scale fault detection method. Gated Graph Convolutional Networks (GatedGCN) analyze topological relationships between adjacent sensors to extract spatial features, while a CNN-Transformer network captures temporal patterns for precise fault identification. Experiments using real flight data show that this method significantly improves FW-UAV flight state cognition accuracy.

• Publication year

  2025

• Venue

  2025 IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA)

• Publication date

  2025-06-28

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICIPCA65645.2025.11138216
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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