Loading...

Developing a neural network model for magnetic yoke structure

Ravanbod, H ; Sharif University of Technology | 2008

934 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/CIMSA.2008.4595836
  3. Publisher: 2008
  4. Abstract:
  5. 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. ©2008 IEEE
  6. Keywords:
  7. Artificial neural network ; Computational intelligence ; Design Parameters ; Finite element method FEM ; Flaw detections ; Geometrical designs ; Innovative methods ; International conferences ; Magnetic flux leakage ; Magnetic yoke ; Measurement systems ; Modeling ; Neural network modelling ; New design ; Oil transmission pipelines ; Permanent magnets ; Time consuming ; Yoke structure ; Artificial intelligence ; Backpropagation ; Flux pinning ; Gas industry ; Image classification ; Intelligent control ; Leakage (fluid) ; Magnetic devices ; Magnetic fields ; Magnetic flux ; Magnetic leakage ; Magnetic materials ; Magnetic structure ; Magnetism ; Magnets ; Materials science ; Neural networks ; Pipelines ; Finite element method
  8. Source: 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, IEEE CIMSA 2008, Istanbul, 14 July 2008 through 16 July 2008 ; 2008 , Pages 75-78 ; 9781424423064 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4595836