DocumentCode :
2527016
Title :
Developing a neural network model for magnetic yoke structure
Author :
Ravanbod, Hossein ; Norouzi, Ehsan
Author_Institution :
Electron. Res. Center, Sharif Univ. of Technol., Tehran
fYear :
2008
fDate :
14-16 July 2008
Firstpage :
75
Lastpage :
78
Abstract :
Magnetic flux leakage technique is used extensively to detect and characterize defects in natural gas and oil transmission pipelines. The amount of magnetic flux introduced into the test sample is an important factor in the resolution of flaw detection. It depends on the power of permanent magnets and the geometrical design of the magnetic yoke. Finite element method (FEM) is the most widely used method of analyzing magnetic yoke due to its power, accuracy and straightforwardness. On the other hand its calculations are so complicated and time consuming, and every single modification in the parameters of the problem requires a new run. In this paper, we present an innovative method to overcome the problem of heavy calculations. In this method an artificial neural network (ANN) is trained to simulate the behavior of the magnetic yoke for different design parameters with an acceptable error. Afterwards the trained ANN calculates the desired output (usually generated flux) for a new design of the yoke by generalization of the already seen samples. This new method has got two advantages over the traditional FEM. First it is very fast and second it is flexible due to modifications in parameters.
Keywords :
computational electromagnetics; finite element analysis; flaw detection; magnetic flux; neural nets; permanent magnets; artificial neural network; finite element method; flaw detection; magnetic flux leakage technique; magnetic yoke structure; neural network model; permanent magnets; pipelines; Artificial neural networks; Gas detectors; Leak detection; Magnetic flux; Magnetic flux leakage; Natural gas; Neural networks; Petroleum; Pipelines; Testing; Artificial Neural Network; Finite Element Method; Magnetic Yoke; Magnetic flux leakage; Modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, 2008. CIMSA 2008. 2008 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-2305-7
Electronic_ISBN :
978-1-4244-2306-4
Type :
conf
DOI :
10.1109/CIMSA.2008.4595836
Filename :
4595836
Link To Document :
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