AE-DRSN-ABHFA-Based Method for AUV Fault Diagnosis

Reliable fault diagnosis of autonomous underwater vehicles (AUVs) remains challenging because fault-sensitive patterns can be obscured by measurement noise, while several fault categories may exhibit similar responses and ambiguous decision boundaries. To address these issues, this study proposes an attention-enhanced deep residual shrinkage network with adaptive boundary-aware hard feature augmentation (AE-DRSN-ABHFA). First, channel and temporal attention mechanisms are integrated into a deep residual shrinkage network (DRSN) to jointly suppress noise-related responses and emphasize fault-sensitive information across sensor and temporal dimensions. Second, the proposed adaptive boundary-aware hard feature augmentation (ABHFA) mechanism identifies ambiguous boundary samples using class prototypes and boundary-aware scores, while class-level difficulty feedback is used to determine the class-processing order during augmentation. Directional pseudo-features are then introduced during training to enrich the feature distribution near difficult decision boundaries. A joint objective incorporating pseudo-feature classification and batch-hard triplet constraints is further employed to improve intra-class compactness and inter-class separability. The main comparative, noise-robustness, and core ablation experiments were repeated using five independent random seeds. Under the current experimental setting, AE-DRSN-ABHFA achieves a mean clean-condition Accuracy of 95.27 ± 1.17% and a Macro-F1 of 0.9498 ± 0.0128. Across the four evaluated noisy conditions, the proposed model obtains an average Accuracy of 85.37 ± 2.76%, an average Macro-F1 of 0.8253 ± 0.0317, and an average worst-class Accuracy of 75.39 ± 5.98%, demonstrating favorable overall and class-level diagnostic performance under the evaluated noisy operating conditions. These results demonstrate the potential of the proposed framework for noise-robust multi-class AUV fault diagnosis, although further validation on different AUV platforms and real sea-trial data remains necessary.