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.

中文翻译：

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使用抗噪 ConvMamba 架构对系泊系统进行局部损坏识别和临近预报


系泊缆绳的监测和临近预报对于维持浮式结构的稳定性至关重要。最近，人们提出了数据驱动的系泊监测方法，以识别潜在的系泊损坏，旨在实现数字实时完整性管理。本文提出了一种系泊缆绳健康状况检测和临近预报框架。该框架可以识别系泊缆绳的多个损坏位置和程度，以及各种复杂的多耦合场景。我们提出的方法在所有现有研究中都不依赖于基于经验的人工特征提取，而是采用全自动序列输入，保留完整的序列信息和模式识别，这有助于模型全面掌握系泊恶化模式。大多数现有方法通过忽略环境中的随机性和固有噪声来简化问题。在本文中，我们考虑了模型构建过程中数据源的潜在随机性和不确定性，增强了泛化性和抗噪性。鉴于输入变量的时间序列性质，我们设计了一种新颖的 ConvMamba 架构，它集成了卷积层和 Mamba 块，其中包括多个模块和选择性状态空间模型。这种设计可确保架构保持 RNN 的递归框架特性，同时还受益于 CNN 的并行计算功能。经过消融实验并与其他现有序列模型进行比较，所提出的架构在准确性和效率方面都得到了证明。 此外，由于模型设计稳健，模型在三种不同类型噪声实验的高干扰下保持了令人印象深刻的抗噪精度。对于实际应用，提出了两种策略来改进原始模型并增强抗噪性。虽然这些策略有一定的局限性，但它们提供了进一步优化的潜力。