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Optimization of Preventive Maintenance Strategies for Electrical Equipment on Offshore Oil Support Vessels Based on Predictive Maintenance Algorithms in an Intelligent Platform

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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 Wang

ABSTRACT

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 Oil

CITE 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.

REFERENCES

[1] Qiao S H I, Youwei L I U, Yanhong F, et al. Interpretation of the New Edition of "Code of Condition-Based Maintenance & Test for Electric Equipment"[J]. Modern Electric Power, 2022, 39(5): 623-630.
[2] Wei W, Xue-feng H, Song-gui L E I. Intelligent inspection and maintenance of mechanical and electrical equipment based on MR[J]. Journal of Graphics, 2022, 43(1): 141-157.
[3] Mohammed N A, Abdulateef O F, Hamad A H. An IoT and machine learning-based predictive maintenance system for electrical motors[J]. Journal Européen des Systèmes Automatisés, 2023, 56(4): 651-656.
[4] Yu Hang. Analysis and Insights on Maintenance and Management of Portable Electrical Equipment at Lancaster University [J]. Laboratory Research and Exploration, 2022, 41 (7): 153-156.
[5] Wang Jian. Research on the Operation and Maintenance of Electrical Equipment in Hydropower Stations [J]. Hydropower and Water Resources, 2021, 5 (4): 104-106.
[6] Jin Feng. Discussion on Maintenance and Management of Port Electrical Equipment [J]. Intelligent Buildings and Engineering Machinery, 2023, 5 (9): 55-57.
[7] Zhao Binbin. Key Points for Electrical Maintenance Personnel in Electrical Equipment Installation and Debugging [J]. Paper Equipment and Materials, 2023, 52 (7): 66-68.
[8] Rosales V. Acceptability of Electrical Installation and Maintenance Instructional Trainer[J]. Asia Research Network Journal of Education, 2022, 2(2): 84-101.
[9] Wu Y, Ma X. A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines[J]. Renewable Energy, 2022, 181(1): 554-566.
[10] Cui Y, Bangalore P, Bertling Tjernberg L. A fault detection framework using recurrent neural networks for condition monitoring of wind turbines[J]. Wind Energy, 2021, 24(11): 1249-1262.
[11] Ukato A, Sofoluwe O O, Jambol D D, et al. Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations[J]. World Journal of Advanced Research and Reviews, 2024, 22(1): 1920-1929.
[12] Zhang P, Gao Z, Cao L, et al. Marine systems and equipment prognostics and health management: a systematic review from health condition monitoring to maintenance strategy[J]. Machines, 2022, 10(2): 72-84.
[13] Babayeju O A, Adefemi A, Ekemezie I O, et al. Advancements in predictive maintenance for aging oil and gas infrastructure[J]. World Journal of Advanced Research and Reviews, 2024, 22(3): 252-266. 

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