Next Article in Journal
Mechanism Analysis of the Strong Coastal Current Zone and Abrupt Strong–Current Phenomena in Spring and Summer in the Yangjiang Sea Area of Western Guangdong in the Northwest of the South China Sea
Next Article in Special Issue
Sliding Mode Control with Feedforward Compensation for a Soft Manipulator That Considers Environment Contact Constraints
Previous Article in Journal
Research on Feature Extraction of Ship-Radiated Noise Based on Multiscale Fuzzy Dispersion Entropy
Previous Article in Special Issue
MPC-Based Collaborative Control of Sail and Rudder for Unmanned Sailboat
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMSE
Volume 11
Issue 5
10.3390/jmse11050998
jmse-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks

by
Fengrui Zhang
1,
Dayong Ning
1,*,
Jiaoyi Hou
1,2,*,
Hongwei Du
1,
Hao Tian
1,
Kang Zhang
1 and
Yongjun Gong
1
1
National Center for International Research of Subsea Engineering Technology and Equipment, Dalian Maritime University, Dalian 116026, China
2
Liaoning Province Key Laboratory Rescue & Salvage Engineering, Dalian Maritime University, Dalian 116000, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(5), 998; https://doi.org/10.3390/jmse11050998
Submission received: 19 April 2023 / Revised: 2 May 2023 / Accepted: 6 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Technology and Equipment for Underwater Robots)
Browse Figures
Versions Notes

Abstract

Efficiently salvaging shipwrecks is of the utmost importance for safeguarding shipping safety and preserving the marine ecosystem. However, traditional methods find it difficult to salvage shipwrecks in deep water. This article presents a novel salvage technology that involves multiple hydraulic claws for directly catching and lifting a 2500-ton shipwreck at 600 m depth. To ensure lifting stability, a semi-active heave compensation (SAHC) system was employed for each lifter to mitigate the effects of sea waves. However, the response delays arising from the hydraulic, control, and filtering systems resist the heave compensation performance. Predicting the barge motion to mitigate measuring and filtering delays and achieve leading compensation is necessary for the salvage. Therefore, a multivariate long short-term memory (LSTM) based neural network was trained to forecast the barge’s heave and pitch motions, exhibiting satisfactory results for the next 5 s. According to the results of numerical simulations, the proposed LSTM-based motion predictive SAHC system demonstrates remarkable effectiveness in compensating for shipwreck motion.
Keywords: shipwreck salvage; semi-active heave compensation; motion prediction; LSTM neural network; machine learning shipwreck salvage; semi-active heave compensation; motion prediction; LSTM neural network; machine learning

Share and Cite

MDPI and ACS Style

Zhang, F.; Ning, D.; Hou, J.; Du, H.; Tian, H.; Zhang, K.; Gong, Y. Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks. J. Mar. Sci. Eng. 2023, 11, 998. https://doi.org/10.3390/jmse11050998

AMA Style

Zhang F, Ning D, Hou J, Du H, Tian H, Zhang K, Gong Y. Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks. Journal of Marine Science and Engineering. 2023; 11(5):998. https://doi.org/10.3390/jmse11050998

Chicago/Turabian Style

Zhang, Fengrui, Dayong Ning, Jiaoyi Hou, Hongwei Du, Hao Tian, Kang Zhang, and Yongjun Gong. 2023. "Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks" Journal of Marine Science and Engineering 11, no. 5: 998. https://doi.org/10.3390/jmse11050998

APA Style

Zhang, F., Ning, D., Hou, J., Du, H., Tian, H., Zhang, K., & Gong, Y. (2023). Semi-Active Heave Compensation for a 600-Meter Hydraulic Salvaging Claw System with Ship Motion Prediction via LSTM Neural Networks. Journal of Marine Science and Engineering, 11(5), 998. https://doi.org/10.3390/jmse11050998

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop