收藏切换
Data-driven algorithm for diving process modelling and anomaly detection of deep-sea pressurized spherical shell
收藏切换
PDF
Ji YAO1, 2, 3, Xue-liang WANG1, 2, 3, Cong YE1, 2, 3, Xue-kang GU1, 2, Hao-zheng CHEN1, 2, 3, Lei WANG1, 2, 3, Zheng-zheng ZHANG1, 2, 3
Journal of Ship Mechanics | 2024, 28(11) : 1710 - 1720
Less
收藏切换
Journal of Ship Mechanics | 2024, 28(11): 1710-1720
Structural Mechanics
Data-driven algorithm for diving process modelling and anomaly detection of deep-sea pressurized spherical shell
Full
Ji YAO1, 2, 3, Xue-liang WANG1, 2, 3, Cong YE1, 2, 3, Xue-kang GU1, 2, Hao-zheng CHEN1, 2, 3, Lei WANG1, 2, 3, Zheng-zheng ZHANG1, 2, 3
Affiliations
  • 1.China Ship Scientific Research Center, Wuxi 214082, China
  • 2.Taihu Laboratory of Deepsea Technological Science, Wuxi 214082, China
  • 3.State Key Laboratory of Deep-sea Manned Vehicles, Wuxi 214082, China
Published: 2024-11-20 doi: 10.3969/j.issn.1007-7294.2024.11.008
Outline
收藏切换

Aiming at the difficulty in modelling the diving process of spherical shells, a data-driven algorithm for diving process modelling and anomaly detection of deep-sea pressurized spherical shells was proposed in this paper. Firstly, the spherical shell structures and historical diving data of manned capsules were analyzed. Then, the diving process modelling algorithm was established based on the long short-term memory network (LSTM), taking the diving depth as the input and the key hot spot strain as the output. The deduction results were analyzed and compared with the DNN model and BP model, the derivation error was reduced by 35.89% and 63.80%, respectively. Finally, based on the LSTM model, a data anomaly detection algorithm was proposed. The proposed algorithm can diagnose and correct abnormal data when a sensor fails.

data-driven algorithm  /  the deep-sea pressurized spherical shell  /  LSTM  /  diving process inference  /  anomaly detection
Ji YAO, Xue-liang WANG, Cong YE, Xue-kang GU, Hao-zheng CHEN, Lei WANG, Zheng-zheng ZHANG. Data-driven algorithm for diving process modelling and anomaly detection of deep-sea pressurized spherical shell[J]. Journal of Ship Mechanics, 2024 , 28 (11) : 1710 -1720 . DOI: 10.3969/j.issn.1007-7294.2024.11.008
Year 2024 volume 28 Issue 11
PDF
62
26
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1007-7294.2024.11.008
  • Receive Date:2024-05-28
  • Online Date:2026-03-26
  • Published:2024-11-20
Article Data
Affiliations
History
  • Received:2024-05-28
Funding
Affiliations
    1.China Ship Scientific Research Center, Wuxi 214082, China
    2.Taihu Laboratory of Deepsea Technological Science, Wuxi 214082, China
    3.State Key Laboratory of Deep-sea Manned Vehicles, Wuxi 214082, China
References
Share
https://castjournals.cast.org.cn/joweb/cblx/EN/10.3969/j.issn.1007-7294.2024.11.008
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT