Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models
The stable operation of ship engines is vital for ensuring the safe navigation of autonomous ships. Vibration signal detection is a widely adopted method for monitoring engine conditions. With the rapid advancements in large language models (LLMs), exploring their applications in autonomous ship sys...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-06-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202603-29-03-0008 |
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| _version_ | 1849319470873444352 |
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| author | Yunzhou Zhang Yanghui Tan Shuai Hao Hong Zeng Peisheng Sang Ya Gao |
| author_facet | Yunzhou Zhang Yanghui Tan Shuai Hao Hong Zeng Peisheng Sang Ya Gao |
| author_sort | Yunzhou Zhang |
| collection | DOAJ |
| description | The stable operation of ship engines is vital for ensuring the safe navigation of autonomous ships. Vibration signal detection is a widely adopted method for monitoring engine conditions. With the rapid advancements in large language models (LLMs), exploring their applications in autonomous ship systems has become a key research focus. This study investigates the use of LLMs for analyzing continuous time-series signals in the maritime domain and proposes a novel approach to predicting marine diesel engine vibration signals. A method utilizing prompt templates was designed to transform numerical signals into textual representations, enabling LLMs to process them effectively. The proposed approach was compared with traditional models, including LSTM, RNN, and SVR, in vibration signal prediction tasks. Experimental results demonstrate that the LLM-based method not only outperforms these baselines under certain conditions but also exhibits enhanced robustness in handling missing data. This research offers new insights into integrating LLMs into intelligent monitoring systems for autonomous ships. |
| format | Article |
| id | doaj-art-b7aa3d89be774486bd30cdec8a18a0a9 |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-b7aa3d89be774486bd30cdec8a18a0a92025-08-20T03:50:26ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-06-0129357358310.6180/jase.202603_29(3).0008Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language ModelsYunzhou Zhang0Yanghui Tan1Shuai Hao2Hong Zeng3Peisheng Sang4Ya Gao5Maritime College, Tianjin University of Technology, 300384, Tianjin, ChinaMaritime College, Tianjin University of Technology, 300384, Tianjin, ChinaMaritime College, Tianjin University of Technology, 300384, Tianjin, China. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300072, ChinaMarine Engineering College, Dalian Maritime University, 116026, Dalian, ChinaMaritime College, Tianjin University of Technology, 300384, Tianjin, ChinaMaritime College, Tianjin University of Technology, 300384, Tianjin, ChinaThe stable operation of ship engines is vital for ensuring the safe navigation of autonomous ships. Vibration signal detection is a widely adopted method for monitoring engine conditions. With the rapid advancements in large language models (LLMs), exploring their applications in autonomous ship systems has become a key research focus. This study investigates the use of LLMs for analyzing continuous time-series signals in the maritime domain and proposes a novel approach to predicting marine diesel engine vibration signals. A method utilizing prompt templates was designed to transform numerical signals into textual representations, enabling LLMs to process them effectively. The proposed approach was compared with traditional models, including LSTM, RNN, and SVR, in vibration signal prediction tasks. Experimental results demonstrate that the LLM-based method not only outperforms these baselines under certain conditions but also exhibits enhanced robustness in handling missing data. This research offers new insights into integrating LLMs into intelligent monitoring systems for autonomous ships.http://jase.tku.edu.tw/articles/jase-202603-29-03-0008vibration signal detectionlarge language models (llms)time-series analysismarine diesel engine monitoring |
| spellingShingle | Yunzhou Zhang Yanghui Tan Shuai Hao Hong Zeng Peisheng Sang Ya Gao Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models Journal of Applied Science and Engineering vibration signal detection large language models (llms) time-series analysis marine diesel engine monitoring |
| title | Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models |
| title_full | Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models |
| title_fullStr | Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models |
| title_full_unstemmed | Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models |
| title_short | Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models |
| title_sort | prediction and analysis of ship engine vibration signals based on prompted language models |
| topic | vibration signal detection large language models (llms) time-series analysis marine diesel engine monitoring |
| url | http://jase.tku.edu.tw/articles/jase-202603-29-03-0008 |
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