Application of Artificial Neural Network on Health Monitoring of Offshore Mooring System

The amount of floating offshore structures had been grown rapidly over these few years due to deepwater exploration and production activities, and this increase in number is predicted to remain over the coming years. Due to the catastrophic consequences from offshore mooring system failure and the l...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-05, Vol.1144 (1), p.12035
Main Authors: Yee, X. E., Mohamed, M. A. W., Montasir, O. A.
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Catastrophic events
Damage detection
Failure detection
Floating structures
Mooring
Neural networks
Offshore
Offshore production
Offshore structures
Petroleum engineering
Structural damage
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Summary:The amount of floating offshore structures had been grown rapidly over these few years due to deepwater exploration and production activities, and this increase in number is predicted to remain over the coming years. Due to the catastrophic consequences from offshore mooring system failure and the limitation on the traditional method for failure detection, there is a need for alternative methods for health monitoring of the offshore mooring system. Artificial Intelligence (AI) has acquired recognition in these few years for petroleum engineering approach, especially Artificial Neural Network (ANN) thanks to its potential to solve complex problems with less time-consuming and effort. A review of the application of ANNs on health monitoring of offshore mooring system had been presented in this paper. The ANNs system had demonstrated its capability as a health monitoring tool for offshore floating structures to detect any damaged or broken mooring system.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1144/1/012035