Fault diagnosis method for quadrotor UAV based on ResCNN-LSTM-attention
To address the challenges of difficult fault feature extraction and low diagnostic accuracy caused by noise interference in quadrotor unmanned aerial vehicles (UAVs) under complex flight environments, this paper proposes a UAV fault diagnosis model (ResCNN-LSTM-ATT) that integrates residual convolutional neural network (ResCNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism. The proposed model seeks to concurrently acquire deep spatial features and long-term temporal dependencies from noise-corrupted airborne multi-source sensor time-series data. Specifically, ResCNN is first employed to extract local spatial features from high-dimensional observational data and suppress noise via residual connections and one-dimensional convolutional neural networks. Secondly, a BiLSTM structure is constructed to capture the bidirectional temporal dependencies of the flight data. Subsequently, an attention mechanism is introduced to weight the outputs of the BiLSTM, focusing on key fault time steps. Finally, the fused spatiotemporal features are fed into the classifier. Experimental results on typical UAV fault diagnosis tasks, including motor, gyroscope, and magnetometer faults, demonstrate that the proposed method achieves superior diagnostic accuracy over existing state-of-the-art approaches while maintaining a compact architecture suitable for real-time onboard deployment.