An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy
Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2312 |
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| author | Hamed Majidiyan Hossein Enshaei Damon Howe Yiting Wang |
| author_facet | Hamed Majidiyan Hossein Enshaei Damon Howe Yiting Wang |
| author_sort | Hamed Majidiyan |
| collection | DOAJ |
| description | Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units. |
| format | Article |
| id | doaj-art-e7f145ef7c8d4eaf9efddca68319dcc6 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e7f145ef7c8d4eaf9efddca68319dcc62025-08-20T02:00:23ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212231210.3390/jmse12122312An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy AnalogyHamed Majidiyan0Hossein Enshaei1Damon Howe2Yiting Wang3Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, AustraliaCentre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, AustraliaCentre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, AustraliaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaUnderstanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units.https://www.mdpi.com/2077-1312/12/12/2312sea-state estimationdynamic systemsdeep learningautonomous shipsystem identificationtime series prediction |
| spellingShingle | Hamed Majidiyan Hossein Enshaei Damon Howe Yiting Wang An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy Journal of Marine Science and Engineering sea-state estimation dynamic systems deep learning autonomous ship system identification time series prediction |
| title | An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy |
| title_full | An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy |
| title_fullStr | An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy |
| title_full_unstemmed | An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy |
| title_short | An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy |
| title_sort | integrated framework for real time sea state estimation of stationary marine units using wave buoy analogy |
| topic | sea-state estimation dynamic systems deep learning autonomous ship system identification time series prediction |
| url | https://www.mdpi.com/2077-1312/12/12/2312 |
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