Optimization of Preventive Maintenance Strategies for Electrical Equipment on Offshore Oil Support Vessels Based on Predictive Maintenance Algorithms in an Intelligent Platform
DOI: 10.23977/jeeem.2024.070303 | Downloads: 14 | Views: 270
Author(s)
Changsheng Wang 1
Affiliation(s)
1 China Oilfield Services Limited Zhanjiang Branch, Zhanjiang, Guangdong, 524047, China
Corresponding Author
Changsheng WangABSTRACT
The electrical equipment of offshore oil support ships operates in harsh marine environments with high failure rates, posing challenges to the development of preventive maintenance strategies. To address this issue, this article proposes an intelligent platform based on Long Short Term Memory (LSTM) algorithm to optimize preventive maintenance strategies for electrical equipment. Firstly, operational data of ship electrical equipment is collected, and data preprocessing and key feature extraction are carried out; subsequently, a prediction model based on LSTM is constructed to make real-time predictions on the health status of the equipment; then, the predictive model is integrated into the intelligent platform to achieve real-time monitoring of device status and dynamic optimization of maintenance strategies. The experimental results show that the LSTM based prediction model outperforms support vector regression (SVR) and random forest methods in terms of prediction accuracy and robustness. The average monthly failure rate of the equipment is 0.67 times, and the maintenance cost for 12 months is only $4750. In the above data conclusions, the intelligent platform based on LSTM algorithm can significantly improve the effectiveness of preventive maintenance strategies for marine oil support ship electrical equipment, highlighting the advantages of the proposed method.
KEYWORDS
Predictive Maintenance; LSTM Network; Electrical Equipment Maintenance; Offshore OilCITE THIS PAPER
Changsheng Wang, Optimization of Preventive Maintenance Strategies for Electrical Equipment on Offshore Oil Support Vessels Based on Predictive Maintenance Algorithms in an Intelligent Platform. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 18-27. DOI: http://dx.doi.org/10.23977/jeeem.2024.070303.
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