Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm
ObjectiveThe marine diesel generator (DG) power distribution system is crucial for ship navigation. However, due to the harsh marine environment, frequent failures occur. Therefore, a fault diagnosis method based on whale optimization algorithm-optimized random forest (WOA-RF) is proposed for the ma...
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| Format: | Article |
| Language: | English |
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Editorial Office of Chinese Journal of Ship Research
2025-04-01
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| Series: | Zhongguo Jianchuan Yanjiu |
| Subjects: | |
| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04193 |
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| _version_ | 1850174668501155840 |
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| author | Weibo LI Feng GAO Peng XIAO Kangzheng HUANG Daojie RUAN Junzhuo GAO |
| author_facet | Weibo LI Feng GAO Peng XIAO Kangzheng HUANG Daojie RUAN Junzhuo GAO |
| author_sort | Weibo LI |
| collection | DOAJ |
| description | ObjectiveThe marine diesel generator (DG) power distribution system is crucial for ship navigation. However, due to the harsh marine environment, frequent failures occur. Therefore, a fault diagnosis method based on whale optimization algorithm-optimized random forest (WOA-RF) is proposed for the marine DG power distribution system.MethodsThe marine DG power distribution system model is built using Matlab/Simulink simulation software. First, fault and normal condition data are collected. Then, the collected data is normalized, time-domain features are extracted, and important features are selected using random forest to reduce data dimensionality. Finally, the WOA-optimized random forest model is used for fault identification, diagnosis and classification.ResultsSimulation results show that the WOA-RF method can identify fault and normal states with 100% accuracy. It can classify 12 different fault types with an accuracy of 98.26%. In the original dataset, the accuracy of WOA-RF improved by at least 4.86% and by up to 34.37% compared to nine different algorithms. In the dataset with 10 dB noise, the accuracy of WOA-RF improved by at least 2.43% and by up to 18.40% compared to six different algorithms.ConclusionThe WOA-RF-based fault diagnosis method demonstrates superior accuracy and robustness in complex marine environments, providing a reliable solution for fault identification in marine power systems. |
| format | Article |
| id | doaj-art-b8fce25938434b33bf55fa8013746fed |
| institution | OA Journals |
| issn | 1673-3185 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj-art-b8fce25938434b33bf55fa8013746fed2025-08-20T02:19:37ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852025-04-01202778810.19693/j.issn.1673-3185.04193ZG4193Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithmWeibo LI0Feng GAO1Peng XIAO2Kangzheng HUANG3Daojie RUAN4Junzhuo GAO5School of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaObjectiveThe marine diesel generator (DG) power distribution system is crucial for ship navigation. However, due to the harsh marine environment, frequent failures occur. Therefore, a fault diagnosis method based on whale optimization algorithm-optimized random forest (WOA-RF) is proposed for the marine DG power distribution system.MethodsThe marine DG power distribution system model is built using Matlab/Simulink simulation software. First, fault and normal condition data are collected. Then, the collected data is normalized, time-domain features are extracted, and important features are selected using random forest to reduce data dimensionality. Finally, the WOA-optimized random forest model is used for fault identification, diagnosis and classification.ResultsSimulation results show that the WOA-RF method can identify fault and normal states with 100% accuracy. It can classify 12 different fault types with an accuracy of 98.26%. In the original dataset, the accuracy of WOA-RF improved by at least 4.86% and by up to 34.37% compared to nine different algorithms. In the dataset with 10 dB noise, the accuracy of WOA-RF improved by at least 2.43% and by up to 18.40% compared to six different algorithms.ConclusionThe WOA-RF-based fault diagnosis method demonstrates superior accuracy and robustness in complex marine environments, providing a reliable solution for fault identification in marine power systems.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04193marine diesel power distribution systemfailure analysisfault diagnosiswhale optimization algorithm (woa)random forest (rf)simulink modelfeature extraction |
| spellingShingle | Weibo LI Feng GAO Peng XIAO Kangzheng HUANG Daojie RUAN Junzhuo GAO Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm Zhongguo Jianchuan Yanjiu marine diesel power distribution system failure analysis fault diagnosis whale optimization algorithm (woa) random forest (rf) simulink model feature extraction |
| title | Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm |
| title_full | Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm |
| title_fullStr | Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm |
| title_full_unstemmed | Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm |
| title_short | Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm |
| title_sort | fault diagnosis of ship diesel power distribution system based on woa rf algorithm |
| topic | marine diesel power distribution system failure analysis fault diagnosis whale optimization algorithm (woa) random forest (rf) simulink model feature extraction |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04193 |
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