Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture

Monitoring and nowcasting of mooring line are of paramount significance for maintaining the stability of floating structure. Recently, data-driven approaches for mooring monitoring have been proposed to identify potential mooring damage, aiming to achieve digital real-time integrity management. This paper proposes a framework for detection and nowcasting of health status of mooring line. The framework can identify multiple damage locations and degrees of mooring line, as well as various complicated multi-coupled scenarios. Our proposed method does not rely on experience-based manual feature extraction in all existing studies, but instead uses fully automatic sequence input, retaining complete series information and pattern recognition, which helps the model comprehensively grasp mooring deterioration patterns. Most existing methods simplify the problem by ignoring randomness and inherent noise in environments. In this paper, we account for the potential randomness and uncertainty of the data source during model construction, enhancing generalizability and noise resistance. Given the time series nature of the input variables, we have designed a novel ConvMamba architecture that integrates the convolutional layers and Mamba block, which includes multiple modules and selective state space model. This design ensures the architecture maintains the recurrent framework characteristic of RNNs while also benefiting from the parallel computing capabilities of CNNs. After ablation experiments and comparisons with other existing sequence models, the superiority of proposed architecture is demonstrated in both accuracy and efficiency. Furthermore, model maintains impressive noise-resistant accuracy under high interference from three different types of noise experiments, attributable to the robust model design. For the practical applications, two strategies are proposed to improve the original model and bolster noise resistance. While these strategies have certain limitations, they offer potential for further optimization.