A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion

doi:10.1016/j.ress.2024.109970

CORC > 力学研究所 > 中国科学院力学研究所 > 高温气体动力学国家重点实验室

	A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion
	Mao, Yixuan 4; Li, Xiaorong 4; Duan, Menglan 3,4; Feng, Yongcun 2; Wang, Jinjia 4; Men HY(门弘远)1; Yang, Heng 4
刊名	RELIABILITY ENGINEERING & SYSTEM SAFETY
	2024-05-01
卷号	245 页码:21
关键词	Mooring line anomaly detection Residual network Feature fusion Semi-submersible platform
ISSN号	0951-8320
DOI	10.1016/j.ress.2024.109970
通讯作者	Li, Xiaorong(xiaorongli@cup.edu.cn)
英文摘要	The structural safety of mooring line is of paramount importance for maintaining the stability of floating structure and personnel health. Once mooring line failure occurs, it may lead to catastrophic consequences. Realtime monitoring and damage identification of mooring line integrity provide an early warning and response to mitigate potential risks and losses. This paper presents a motion-based mooring line anomaly detection framework, combining continuous wavelet transform, multi-scale feature fusion, and squeeze-and-excitation residual network (namely CWT-FFSeResNet). The framework aims to identify different degrees of mooring line damage in a semi-submersible platform (SEMI). Extensive numerical simulations under various sea conditions provide motion response data for different mooring line damage states. Subsequently, time-series motion data is converted into a time-frequency image, and feature fusion stacks images of three motions from the same time period on channel, forming a whole sample to represent the state of a mooring line. Compared with other existing models, the model shows a perfect performance in terms of accuracy and efficiency. Based on the test results of insufficient samples, the model indicates the potential to be established at a smaller time consuming. In addition, test experiments with different Gaussian noise levels demonstrated relatively satisfactory noise robustness of proposed method.
分类号	二类/Q1
资助项目	National Key Research and Develop- ment Program of China[2016YFC0303701]
WOS关键词	NEURAL-NETWORK ; RELIABILITY ; DESIGN
WOS研究方向	Engineering ; Operations Research & Management Science
语种	英语
WOS记录号	WOS:001179573400001
资助机构	National Key Research and Develop- ment Program of China
其他责任者	Li, Xiaorong
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/94720]
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.Chinese Acad Sci, LHD, Inst Mech, Beijing 100190, Peoples R China 2.China Univ Petr, Coll Petr Engn, Beijing, Peoples R China; 3.Tsinghua Univ, Inst Ocean Engn, Shenzhen Int Grad Sch, Shenzhen, Peoples R China; 4.China Univ Petr, Coll Safety & Ocean Engn, Beijing, Peoples R China;
推荐引用方式 GB/T 7714	Mao, Yixuan,Li, Xiaorong,Duan, Menglan,et al. A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion[J]. RELIABILITY ENGINEERING & SYSTEM SAFETY,2024,245:21.
APA	Mao, Yixuan.,Li, Xiaorong.,Duan, Menglan.,Feng, Yongcun.,Wang, Jinjia.,...&Yang, Heng.(2024).A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion.RELIABILITY ENGINEERING & SYSTEM SAFETY,245,21.
MLA	Mao, Yixuan,et al."A novel mooring system anomaly detection framework for SEMI based on improved residual network with attention mechanism and feature fusion".RELIABILITY ENGINEERING & SYSTEM SAFETY 245(2024):21.