An Adaptive Enhanced Meta-Transformer for Few-Shot Fault Diagnosis of Unmanned Underwater Vehicle Actuators Under Noisy Conditions

Fault diagnosis is crucial for ensuring the navigational safety and reliability of unmanned underwater vehicles (UUVs). However, in complex marine environments, UUV fault samples are typically scarce and often contaminated by severe hydraulic noise, which significantly restricts the performance of existing diagnostic methods. To address these challenges, this paper proposes a few-shot fault diagnosis method based on an Adaptive Enhanced Meta-Transformer. First, operational vibration data from UUV actuators are acquired and preprocessed. Second, a feature enhancement module is constructed using an improved Transformer architecture that incorporates a novel Adaptive Head-Weighted Multi-Head Self-Attention mechanism. This mechanism enables the model to precisely localize key fault segments and enhance directional features, even under noisy backgrounds, effectively mitigating attention dispersion. Subsequently, a meta-optimization strategy is employed to iteratively update model parameters, enabling the model to rapidly adapt to new tasks with limited data. Finally, extensive experiments using real-world operational data from UUV actuators demonstrate that the proposed method outperforms state-of-the-art baselines in terms of accuracy and robustness, particularly in cross-component and noisy scenarios.