Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture
文献类型：期刊论文
| 作者 | Mao, Yixuan; Duan, Menglan ; Men HY(门弘远); Zheng, Miaozi
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| 刊名 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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| 出版日期 | 2025-02 |
| 卷号 | 224页码:112092 |
| 关键词 | Semi-submersible platform Mooring line Anomaly identification and nowcasting Sequence deep learning Structural health monitoring |
| ISSN号 | 0888-3270 |
| DOI | 10.1016/j.ymssp.2024.112092 |
| 英文摘要 | 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 noiseresistant 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. |
| 分类号 | 一类 |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001357242900001 |
| 资助机构 | National Natural Science Foundation of China {52301325] |
| 其他责任者 | Duan ML |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/97207]  |
| 专题 | 力学研究所_高温气体动力学国家重点实验室 |
| 作者单位 | 1.【Zheng, Miaozi】 Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China 2.【Men, Hongyuan】 Chinese Acad Sci, Inst Mech, LHD, Beijing, Peoples R China 3.【Duan, Menglan】 Tsinghua Univ, Inst Ocean Engn, Shenzhen Int Grad Sch, Shenzhen, Peoples R China 4.【Mao, Yixuan & Duan, Menglan】 China Univ Petr, Coll Safety & Ocean Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Mao, Yixuan,Duan, Menglan,Men HY,et al. Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2025,224:112092. |
| APA | Mao, Yixuan,Duan, Menglan,门弘远,&Zheng, Miaozi.(2025).Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,224,112092. |
| MLA | Mao, Yixuan,et al."Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 224(2025):112092. |
入库方式： OAI收割
来源：力学研究所
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