A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines
- 1 July 2016
- book chapter
- other
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 32 references indexed in Scilit:
- Artificial neural network models for predicting condition of offshore oil and gas pipelinesAutomation in Construction, 2014
- A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clusteringInformation Sciences, 2013
- ANFIS-based approach for predicting sediment transport in clean sewerApplied Soft Computing, 2012
- ANN and ANFIS performance prediction models for hydraulic impact hammersTunnelling and Underground Space Technology, 2012
- Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defectsNDT & E International, 2009
- A Space Mapping Methodology for Defect Characterization From Magnetic Flux Leakage MeasurementsIEEE Transactions on Magnetics, 2008
- MFL signals and artificial neural networks applied to detection and classification of pipe weld defectsNDT & E International, 2006
- Characterization of gas pipeline inspection signals using wavelet basis function neural networksNDT & E International, 2000
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Ten Lectures on WaveletsPublished by Society for Industrial & Applied Mathematics (SIAM) ,1992