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/1144/1/012035Additional details
Identifiers
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