A UAV fault diagnosis model based on end-to-end causal inference and attention mechanism

Abstract Failures of Unmanned Aerial Vehicle (UAV) can lead to property damage and casualties, making it imperative to preemptively diagnose failures. Most existing methods do not adequately address the temporal consistency of flight sequence data and often overlook the issue of data imbalance among various fault types. To address these shortcomings, this paper proposes an end-to-end model (CCTFANet) based on the Gated Recurrent Unit (GRU) that ensures temporal consistency of sequence data by maintaining overall causality of the model. Specifically, dual-path causal convolution is used for initial feature extraction from enhanced temporal signals to capture local features at different scales. Subsequently, a temporal feature fusion attention mechanism is introduced as an interactive bridge linking features of different scales, modeling channel dependencies. The GRU is employed for temporal modeling and global feature extraction of fused features to capture long-term dependencies, while maintaining temporal consistency of features in the final stage to ensure the overall causality of the model. This study, utilizing the ALFA dataset, employs time warping and dynamic step sliding window techniques to reconstruct the dataset and alleviate the data imbalance problem. The effectiveness and robustness of the proposed method are validated on the reconstructed dataset. Experimental results demonstrate that the fault diagnosis model can effectively identify UAV faults with a precision of 96.93%. Even under varying noise levels, the proposed method maintains high diagnostic accuracy.