Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF

Hui Ouyang, Weibo Li (), Feng Gao, Kangzheng Huang and Peng Xiao
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Hui Ouyang: Wuhan Second Ship Design and Research Institute, Wuhan 430064, China
Weibo Li: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Feng Gao: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Kangzheng Huang: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Peng Xiao: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Energies, 2024, vol. 17, issue 22, 1-18

Abstract: 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.

Keywords: ship diesel power system; fault diagnosis; IVY; random forests (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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