A digital twin comprehensive monitoring system for ship equipment

• Autorzy: Miao Zhe , Zhao Yong , Su Shaojuan , Song Nanzhe

Identyfikatory

DOI

10.2478/pomr-2024-0055

• Języki publikacji: EN

Abstrakty

EN

In this study, a comprehensive digital twin monitoring system for ship equipment was designed and implemented, including the system architecture, key technologies, and applications. Through data-driven models and operational monitoring system analysis, our PSO-SVM-based time series prediction method demonstrated excellent predictive capabilities for catamaran equipment, achieving efficient fault warnings using a threshold method. The digital twin model and virtual scenarios constructed here provide a visualisation and simulation platform for equipment status monitoring, enhanced fault diagnosis and support for maintenance decisions. The system integrates real-time monitoring, fault warning, and data analysis, and testing results show good stability and accuracy. In addition, the system optimises the user experience through multi-round feedback testing, and ensures data security and privacy protection through multi-layer encryption, identity verification, and role-based access control. A case study indicates that the proposed system effectively monitors equipment status and provides fault warnings, and has broad application prospects and practical value. Future work will focus on optimising the functionality and improving the applicability and security of the system.

Słowa kluczowe

EN

various ship equipment; digital twin; comprehensive monitoring system; security privacy protection

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2024
• Tom: nr 4
• Strony: 111--121
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Miao Zhe

• Dalian Maritime University, China

autor

Zhao Yong

• Dalian Maritime University, China

• https://orcid.org/0000-0001-8216-9210

autor

Su Shaojuan

• Dalian Maritime University, China

autor

Song Nanzhe

• Dalian Maritime University, China

Bibliografia

• 1. Lv Z, Lv H, Fridenfalk M. Digital twins in the marine industry. Electronics 2023, 12(9), 2025. https://doi.org/10.3390/electronics12092025.
• 2. Madusanka NS, Fan Y, Yang S, Xiang X. Digital twin in the maritime domain: A review and emerging trends. Journal of Marine Science and Engineering 2023, 11(5), 1021. https://doi.org/10.3390/jmse11051021.
• 3. Zhong D, Xia Z, Zhu Y, Duan J. Overview of predictive maintenance based on digital twin technology. Heliyon 2023, 9(4). https://doi.org/10.1016/j.heliyon.2023.e14534.
• 4. Karatuğ C, Arslanoğlu Y, Soares CG. Review of maintenance strategies for ship machinery systems. Journal of Marine Engineering & Technology 2023, 22(5), 233-47. https://doi.org/10.1080/20464177.2023.2180831.
• 5. Hasan A, Asfihani T, Osen O, Bye RT. Leveraging digital twins for fault diagnosis in autonomous ships. Ocean Engineering 2024, 292, 116546. https://doi.org/10.1016/j.oceaneng.2023.116546.
• 6. Kinaci OK. Ship digital twin architecture for optimizing sailing automation. Ocean Engineering 2023, 275, 114128.https://doi.org/10.1016/j.oceaneng.2023.114128.
• 7. Liu Y, Ren H. Rapid acquisition method for structural strength evaluation stresses of the ship digital twin model. Ocean Engineering 2023, 285, 115323. https://doi.org/10.1016/j.oceaneng.2023.115323.
• 8. Li Y, Zhang W, Cui L, Gao H. System reliability modeling and analysis for a marine power equipment operating in a discrete‐time dynamic environment. Quality and Reliability Engineering International 2024, 40(6), 3422-38. https://doi.org/10.1002/qre.3577.
• 9. Zhou Q, Li H, Zeng X, Li L, Cui S, Du Z. A quantitative safety assessment for offshore equipment evaluation using fuzzy FMECA: A case study of the hydraulic submersible pump system. Ocean Engineering 2024, 293, 116611. https://doi.org/10.1016/j.oceaneng.2023.116611.
• 10. Deng J, Liu S, Shu Y, Hu Y, Xie C, Zeng X. Risk evolution and prevention and control strategies of maritime accidents in China’s coastal areas based on complex network models. Ocean & Coastal Management 2023, 237, 106527. https://doi.org/10.1016/j.ocecoaman.2023.106527.
• 11. Zhang D. Fault diagnosis of ship power equipment based on adaptive neural network. International Journal of Emerging Electric Power Systems 2022, 23(6), 779-91. https://doi.org/10.1515/ijeeps-2022-0103.
• 12. Nejad AR, Purcell E, Valavi M, Hudak R, Lehmann B, Gutierrez Guzman F, et al. Condition monitoring of ship propulsion systems: State-of-the-art, development trend and role of digital twin.International Conference on Offshore Mechanics and Arctic Engineering: American Society of Mechanical Engineers, 2021. V007T07A05. https://doi.org/10.1115/OMAE2021-61847.
• 13. Lee S, Lee T, Kim J, Lee J, Ryu K, Kim Y, et al. A study on the application of discrete wavelet decomposition for fault diagnosis on a ship oil purifier. Processes 2022, 10(8), 1468. https://doi.org/10.3390/pr10081468.
• 14. Kang Y-J, Noh Y, Jang M-S, Park S, Kim J-T. Hierarchical level fault detection and diagnosis of ship engine systems. Expert Systems with Applications 2023, 213, 118814. https://doi.org/10.1016/j.eswa.2022.118814.
• 15. Karatuğ C, Arslanoğlu Y, Soares CG. Design of a decision support system to achieve condition-based maintenance in ship machinery systems. Ocean Engineering 2023, 281, 114611. https://doi.org/10.1016/j.oceaneng.2023.114611.
• 16. Ji Z, Gan H, Liu B. A deep learning-based fault warning model for exhaust temperature prediction and fault warning of marine diesel engine. Journal of Marine Science and Engineering 2023, 11(8), 1509. https://doi.org/10.3390/jmse11081509.
• 17. Duan X, Gao Z, Qiao Z, Du T, Zou Y, Zhang P, et al. A study of adaptive threshold based on the reconstruction model for marine systems and their equipment failure warning. Journal of Marine Science and Engineering 2024, 12(5), 742. https://doi.org/10.3390/jmse12050742.
• 18. Whaiduzzaman M, Sakib A, Khan NJ, Chaki S, Shahrier L, Ghosh S, et al. Concept to reality: An integrated approach to testing software user interfaces. Applied Sciences 2023, 13(21), 11997. https://doi.org/10.3390/app132111997.
• 19. Pushpakumar R, Sanjaya K, Rathika S, Alawadi AH, Makhzuna K, Venkatesh S, et al. Human-computer interaction: Enhancing user experience in interactive systems. E3S Web of Conferences: EDP Sciences, 2023, 04037. https://doi.org/10.1051/e3sconf/202339904037.
• 20. Sharma R, Arya R. Security threats and measures in the Internet of Things for smart city infrastructure: A state of art. Transactions on Emerging Telecommunications Technologies 2023, 34(11), e4571. https://doi.org/10.1002/ett.4571.
• 21. Sheng B, Yin X, Zhang C, Zhao F, Fang Z, Xiao Z. A rapid virtual assembly approach for 3D models of production line equipment based on the smart recognition of assembly features. Journal of Ambient Intelligence and Humanized Computing 2019, 10, 1257-70. https://doi.org/10.1007/s12652-018-0753-z.
• 22. Liu X, Jiang D, Tao B, Xiang F, Jiang G, Sun Y, et al. A systematic review of digital twin about physical entities, virtual models, twin data, and applications. Advanced Engineering Informatics 2023, 55, 101876. https://doi.org/10.1016/j.aei.2023.101876.
• 23. Chu C-H, Liu Y-L. Augmented reality user interface design and experimental evaluation for human-robot collaborative assembly. Journal of Manufacturing Systems 2023, 68, 313-24. https://doi.org/10.1016/j.jmsy.2023.04.007.
• 24. Katipoğlu OM, Yeşilyurt SN, Dalkılıc HY, Akar F. Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows. Environmental Monitoring and Assessment 2023, 195(9), 1108. https://doi.org/10.1007/s10661-023-11700-0.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).