Fine-tuning DSAE-based anomaly-locating model of underwater mooring system

被引：0
|
作者：
Yao, Ji [1 ,2 ,4 ]
Wang, Xueliang [1 ,2 ,4 ]
Gu, Xuekang [1 ,2 ,4 ]
Wu, Wenhua [3 ]
Chen, Haozheng [1 ,2 ,4 ]
机构：
[1] China Ship Sci Res Ctr, Wuxi, Jiangsu, Peoples R China
[2] Taihu Lab Deepsea Technol Sci, Wuxi, Jiangsu, Peoples R China
[3] Dalian Univ Technol, Dept Engn Mech, Dalian, Liaoning, Peoples R China
[4] Natl Key Lab Ship Struct Safety, Wuxi, Jiangsu, Peoples R China
来源：
OCEAN ENGINEERING | 2024年 / 309卷
关键词：
Anomaly-locating; Underwater mooring system; DSAE; Fine-tuning; Damage degree; PREDICTION;
D O I：
10.1016/j.oceaneng.2024.118443
中图分类号：
U6 [水路运输]; P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
Aiming at the difficulty of anomaly locating of the underwater mooring system, this paper develops an anomalylocating algorithm based on the Deep Stark Auto-encoder (DSAE) network. Specifically, based on the real design parameters of a semi-submersible platform, a hydrodynamic model was established. Damage degrees of 5%was selected based on the DNV standard. The analysis of the responses was carried out when the anchor chain was damaged in different positions. Then an anomaly-locating model was established based on the DSAE method. The locating accuracy reached 99.13%. In contrast, the complete states were recognized as the damaged state. Furthermore, the established model was retrained by the fine-tuning operation. The locating accuracy reached 100% which can guide the safe service of the platform.
引用
收藏
页数：9
相关论文
共 50 条
  • [31] MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
    Rafailov, Rafael
    Hatch, Kyle
    Kolev, Victor
    Martin, John D.
    Phielipp, Mariano
    Finn, Chelsea
    CONFERENCE ON ROBOT LEARNING, VOL 229, 2023, 229
  • [32] Lightweight model-based two-step fine-tuning for fault diagnosis with limited data
    Tang, Tang
    Wu, Jie
    Chen, Ming
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (12)
  • [33] Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
    He, Weiguo
    Yin, Deyang
    Zhang, Kaifeng
    Zhang, Xiangwen
    Zheng, Jianyong
    ENERGIES, 2021, 14 (14)
  • [34] A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries
    Chou, Jia-Hong
    Wang, Fu-Kwun
    Lo, Shih-Che
    BATTERIES-BASEL, 2023, 9 (06):
  • [35] Bus routing fine-tuning for integrated network-based demand and bus bridging for a disrupted railway system
    Feng, Yinfei
    Ceder, Avishai
    Zhang, Silin
    Cao, Zhichao
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 242
  • [36] An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction
    He, Zhiquan
    Zhang, Lujun
    Wang, Hengyou
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2023, 17
  • [37] A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection
    Kaya, Yasin
    Gursoy, Ercan
    SOFT COMPUTING, 2023, 27 (09) : 5521 - 5535
  • [38] MULTIPLE CURVATURE BASED APPROACH TO HUMAN UPPER BODY PARTS DETECTION WITH CONNECTED ELLIPSE MODEL FINE-TUNING
    Da Xu, Richard Yi
    Kemp, Michael
    2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, : 2577 - 2580
  • [39] Instance segmentation of mouse brain scanning electron microscopy images based on fine-tuning nature image model
    Cheng, Ao
    Zhao, Guoqiang
    Zhang, Ruobing
    Wang, Lirong
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2024, 32 (18): : 2836 - 2845
  • [40] Enhancing text understanding of decoder-based model by leveraging parameter-efficient fine-tuning method
    Feroze, Wasif
    Cheng, Shaohuan
    Jimale, Elias Lemuye
    Jakhro, Abdul Naveed
    Qu, Hong
    Neural Computing and Applications, 2025, 37 (09) : 6899 - 6913
12345