Author(s)

Changsheng Wang ¹

Affiliation(s)

¹ China Oilfield Services Limited Zhanjiang Branch, Zhanjiang, Guangdong, 524047, China

Corresponding Author

Changsheng Wang

ABSTRACT

In the operation of offshore oil support ships, due to the low accuracy of fault diagnosis for the side thrust system and the inability to accurately eliminate faults, untimely diagnosis combined with incorrect fault diagnosis can easily lead to serious safety hazards and economic losses. The aim of this study is to apply an intelligent platform based on Fault Tree Analysis (FTA) to improve the accuracy and efficiency of fault diagnosis in the lateral push system. This article first uses a data collection system to monitor key parameters in real-time, and combines CNN in deep learning algorithms to predict and diagnose faults, and then constructs a fault tree model for the lateral push system to identify the main fault modes and their causes. The experimental results show that after applying the intelligent platform, the accuracy of fault diagnosis has increased to 92%, which is more than 20% higher than the single fault tree analysis method. The diagnosis time has been shortened by 30%, significantly reducing the risk of ship shutdown. The intelligent platform based on FTA can effectively enhance the fault diagnosis capability of the offshore oil support ship's side push system, providing strong technical support for the safe operation of ships.

KEYWORDS

Fault Tree Analysis; Intelligent Platform; Offshore Oil Supports Ship Side Push Systems; Fault Diagnosis

CITE THIS PAPER

Changsheng Wang, Application of Fault Tree Analysis (FTA) Based Intelligent Platform in Fault Diagnosis of Offshore Oil Support Ship Side Push System. Frontiers in Power and Energy Systems (2024) Vol. 3: 80-88. DOI: http://dx.doi.org/10.23977/fpes.2024.030110.

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Sponsors, Associates, and Links

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• Journal of Metallurgy and Mineral Processing
  
  
• International Journal of Oil and Gas Science and Engineering
  
  
• Journal of Disaster Prevention and Reduction
  
  
• Smart Systems and Green Energy
  
  
• Journal of Mining Engineering and Safety Technology
  
  
• Thermal Power Engineering
  
  
• Energy Conversion Research