Published May 1, 2021 | Version v1
Journal article

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

  • 1. Civil and Environmental Engineering Department, Universiti Teknologi PETRONAS, 31600 Bandar Seri Iskandar, Perak (Malaysia)

Description

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. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/1757-899X/1144/1/012035

Additional details

Publishing Information

Journal Title
IOP Conference Series. Materials Science and Engineering (Online)
Journal Volume
1144
Journal Issue
1
Journal Page Range
[8 p.]
ISSN
1757-899X

Conference

Title
3. International Symposium on Civil and Environmental Engineering
Acronym
ISCEE 2020
Dates
1-2 Dec 2020
Place
Batu Pahat, Johor (Malaysia)

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53088434
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING; S02: PETROLEUM;
Resource subtype / Literary indicator
Conference
Descriptors DEI
ARTIFICIAL INTELLIGENCE; DETECTION; MONITORING; MOORINGS; NEURAL NETWORKS; PETROLEUM
Descriptors DEC
ENERGY SOURCES; FOSSIL FUELS; FUELS