Quantitative Analysis of Circumferential Magnetic Flux Leakage (CMFL) Signal for Oil and Gas Pipeline Based on RBF Neural Network

ICPTT 2011 ◽  
2011 ◽  
Author(s):  
Liqiong Chen ◽  
Lei Cheng ◽  
Guiliang Li ◽  
Sizhong Wang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Magnetic Flux ◽  
Oil And Gas ◽  
Rbf Neural Network ◽  
Gas Pipeline ◽  
Magnetic Flux Leakage ◽  
Flux Leakage

ANSYS Simulation Technology of Pipeline Magnetic Flux Leakage Inspection

2013 ◽  
Vol 718-720 ◽  
pp. 1000-1005
Author(s):  
Li Jian Yang ◽  
Sen Lin Zhang ◽  
Song Wei Gao
Keyword(s):  
Magnetic Field ◽  
Finite Element ◽  
Electromagnetic Field ◽  
Magnetic Flux ◽  
Oil And Gas ◽  
Gas Pipeline ◽  
Magnetic Flux Leakage ◽  
Field Calculation ◽  
Inspection Method ◽  
Flux Leakage

In order to solve the need of the oil and gas pipeline defect quantification in the real-time online defecting, magnetic flux leakage inspection method was applied to oil and gas pipeline inspection. According to the basic theory of the electromagnetic field, finite element solution of electromagnetic field and ANSYS electromagnetic field calculation theory, using the function of ANSYS 's simulation and calculation for magnetic field, three-dimensional finite element model of the oil and gas pipeline defect was built up. Through simulating, the relationship between defect signal and defect size was found, the optimal distance of the hall sensor lift-off value was verified, the best magnetization of leakage magnetic field was discussed, and various factors to influence the magnetic flux leakage signal is analyzed.

Electromagnetic Nondestructive Testing in Cracked Defects of Oil-Gas Casing Based on Ant Colony Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 760-763 ◽  
Author(s):  
Wei Zhang ◽  
Yi Bing Shi ◽  
Yan Jun Li
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Magnetic Flux ◽  
Ant Colony Algorithm ◽  
Fem Simulation ◽  
Ant Colony ◽  
Working Environment ◽  
Magnetic Flux Leakage ◽  
The Neural Network ◽  
Flux Leakage

In this paper, a new method on quantitative analysis of magnetic flux leakage signal by ant colony neural network is proposed. Firstly, the parameters of the magnetic flux leakage signal which can reflect the various characteristics of cracked defects are determined by finite element method (FEM) simulation. Secondly, based on the study of the ant colony algorithm, the neural network model is established for the magnetic flux leakage signals processing. Finally, in the simulated working environment, the performance of the neural network is tested with the different signal features as input. The experimental results proved the feasibility of the ant colony neural network, verified the increases of the convergence rate and the accuracy of the neural network, and improved the efficiency as well as the quality of the quantitative analysis for the magnetic flux leakage signals.

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