Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF
Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper prop...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/22/5799 |
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| _version_ | 1850216988604891136 |
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| author | Hui Ouyang Weibo Li Feng Gao Kangzheng Huang Peng Xiao |
| author_facet | Hui Ouyang Weibo Li Feng Gao Kangzheng Huang Peng Xiao |
| author_sort | Hui Ouyang |
| collection | DOAJ |
| description | Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems. |
| format | Article |
| id | doaj-art-5c68872bc7b8417f864ee511bbda6fe9 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-5c68872bc7b8417f864ee511bbda6fe92025-08-20T02:08:11ZengMDPI AGEnergies1996-10732024-11-011722579910.3390/en17225799Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RFHui Ouyang0Weibo Li1Feng Gao2Kangzheng Huang3Peng Xiao4Wuhan Second Ship Design and Research Institute, Wuhan 430064, 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, ChinaShip diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems.https://www.mdpi.com/1996-1073/17/22/5799ship diesel power systemfault diagnosisIVYrandom forests |
| spellingShingle | Hui Ouyang Weibo Li Feng Gao Kangzheng Huang Peng Xiao Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF Energies ship diesel power system fault diagnosis IVY random forests |
| title | Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF |
| title_full | Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF |
| title_fullStr | Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF |
| title_full_unstemmed | Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF |
| title_short | Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF |
| title_sort | research on fault diagnosis of ship diesel generator system based on ivy rf |
| topic | ship diesel power system fault diagnosis IVY random forests |
| url | https://www.mdpi.com/1996-1073/17/22/5799 |
| work_keys_str_mv | AT huiouyang researchonfaultdiagnosisofshipdieselgeneratorsystembasedonivyrf AT weiboli researchonfaultdiagnosisofshipdieselgeneratorsystembasedonivyrf AT fenggao researchonfaultdiagnosisofshipdieselgeneratorsystembasedonivyrf AT kangzhenghuang researchonfaultdiagnosisofshipdieselgeneratorsystembasedonivyrf AT pengxiao researchonfaultdiagnosisofshipdieselgeneratorsystembasedonivyrf |