A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship
To enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/2/384 |
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| author | Lei Guo Tianjian Wang Xiao Dong Peng Zhang Hong Zeng Jundong Zhang |
| author_facet | Lei Guo Tianjian Wang Xiao Dong Peng Zhang Hong Zeng Jundong Zhang |
| author_sort | Lei Guo |
| collection | DOAJ |
| description | To enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies of the intelligent machinery room system, enabling the structured establishment of a hierarchical evaluation index system. The comprehensive weight is derived through synergistic application of the fuzzy set (FS) theory and entropy weight. This process integrated expert-defined functional boundaries with measurable parameters critical to system performance. Then, an improved cloud center of gravity method based on the Gaussian cloud model and sliding time window method is used for the system’s adaptive health value calculation. The dynamic health model can achieve continuous online assessment and track the further evolution of the system. Finally, the proposed model is applied to the Fuel Oil Supply System (FOSS). The integration of system performance output and disassembly inspection results demonstrates that the method proposed in the article more accurately reflects the true health status changes in the system when mapping health values. |
| format | Article |
| id | doaj-art-031c34ec0ede48a3b46a9d5367a22286 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-031c34ec0ede48a3b46a9d5367a222862025-08-20T02:03:39ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113238410.3390/jmse13020384A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent ShipLei Guo0Tianjian Wang1Xiao Dong2Peng Zhang3Hong Zeng4Jundong Zhang5Marine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaMarine Engineering College, Dalian Maritime University, Dalian 116026, ChinaTo enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies of the intelligent machinery room system, enabling the structured establishment of a hierarchical evaluation index system. The comprehensive weight is derived through synergistic application of the fuzzy set (FS) theory and entropy weight. This process integrated expert-defined functional boundaries with measurable parameters critical to system performance. Then, an improved cloud center of gravity method based on the Gaussian cloud model and sliding time window method is used for the system’s adaptive health value calculation. The dynamic health model can achieve continuous online assessment and track the further evolution of the system. Finally, the proposed model is applied to the Fuel Oil Supply System (FOSS). The integration of system performance output and disassembly inspection results demonstrates that the method proposed in the article more accurately reflects the true health status changes in the system when mapping health values.https://www.mdpi.com/2077-1312/13/2/384intelligent shipcloud barycentercondition assessmenthealth modelreliabilityship systems |
| spellingShingle | Lei Guo Tianjian Wang Xiao Dong Peng Zhang Hong Zeng Jundong Zhang A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship Journal of Marine Science and Engineering intelligent ship cloud barycenter condition assessment health model reliability ship systems |
| title | A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship |
| title_full | A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship |
| title_fullStr | A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship |
| title_full_unstemmed | A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship |
| title_short | A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship |
| title_sort | dynamic cloud center of gravity model for real time system level health status assessment of intelligent ship |
| topic | intelligent ship cloud barycenter condition assessment health model reliability ship systems |
| url | https://www.mdpi.com/2077-1312/13/2/384 |
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