Reliable fault diagnosis for autonomous underwater vehicle (AUV) propellers is critical yet highly challenging in complex underwater environments. Diagnostic methods based on single optical video or electrical signals exhibit inherent limitations. Moreover, traditional multimodal fusion strategies suffer from a sharp performance decline when facing dynamic degradation of optical data quality (e.g., blur, low-light) due to the lack of a reliability assessment mechanism. To address this bottleneck, this paper proposes the quality-aware conditional modality gating attention network (QACMANet). This network introduces a dual-dimensional reliability assessment framework that integrates external objective image-quality cues with internal subjective model uncertainty. The mechanism synergistically evaluates data quality and model confidence, adaptively reducing reliance on optical data when it is unreliable and intelligently shifting reliance to the more stable electrical signals. Extensive experiments on a multimodal dataset featuring 11 test conditions demonstrate that QACMANet achieves an average accuracy of 92.86%, outperforming the best baseline by a significant margin of 7.78 percentage points, with the advantage being particularly pronounced under severe visual degradation. This research provides a robust solution for multimodal diagnosis in non-ideal underwater environments and validates the critical value of explicit reliability assessment to enhance the environmental adaptability of the system.
Zhao et al. (Fri,) studied this question.