Magnetic flux leakage testing defect reconstruction method based on improved particle swarm optimization algorithm

A particle swarm optimization and magnetic flux leakage detection technology, applied in neural learning methods, material magnetic variables, biological neural network models, etc., can solve problems such as inability to accurately reconstruct the true contour of defects and trapping in local optimum.

Active Publication Date: 2015-09-09
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

[0004] As a powerful stochastic evolutionary algorithm, PSO can be used to find the global optimal solution in a complex search space, but when solving high-dimensional practical problems, due to the complexity of the problem, it is e

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  • Magnetic flux leakage testing defect reconstruction method based on improved particle swarm optimization algorithm
  • Magnetic flux leakage testing defect reconstruction method based on improved particle swarm optimization algorithm
  • Magnetic flux leakage testing defect reconstruction method based on improved particle swarm optimization algorithm

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Embodiment Construction

[0041] The invention adds the self-adaptive variation factor into the EPUS-PSO algorithm, and applies it to the reconstruction method of the magnetic flux leakage defect. The technical solution of the present invention will be described below from the IEPUS-PSO algorithm.

[0042] (1) IEPUS-PSO algorithm

[0043] Particle swarm algorithm, also known as bird swarm algorithm, is an evolutionary algorithm proposed by Dr. Eberhart and Dr. Kennedy in 1995, which originated from the study of bird predation behavior. The algorithm was originally inspired by the regularity of bird cluster activities, and then a simplified model was established using swarm intelligence. Based on the observation of animal cluster activities, the particle swarm optimization algorithm uses the information sharing of individuals in the group to make the movement of the whole group evolve from disorder to order in the problem solution space, so as to obtain the optimal solution.

[0044] The EPUS-PSO algo...

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Abstract

The invention relates to a magnetic flux leakage testing defect reconstruction method based on an improved particle swarm optimization algorithm. According to the method, self-adaption mutagenic factors are introduced into a particle swarm EPUS-PSO algorithm based on an effective population utilization strategy, thus obtaining the IEPUS-PSO algorithm, and then the IEPUS-PSO algorithm is applied to defect reconstruction in magnetic flux leakage testing. With the improved algorithm, the reconstruction accuracy is improved, the computation time is reduced, and the defect outline can be reconstructed by magnetic flux leakage signals for defects of different sizes.

Description

technical field [0001] The present invention relates to a magnetic flux leakage detection technology, in particular to a defect reconstruction method for magnetic flux leakage detection based on an improved efficient population utilization strategy particle swarm optimization (Improved Efficient Population Utilization Strategy for Particle Swarm Optimization, IEPUS-PSO) algorithm. Background technique [0002] In recent years, with the continuous development of my country's economy and the continuous expansion of industrial scale, electric power production has become a pillar industry of our country. Therefore, the application of non-destructive testing technology in the production and maintenance of power equipment has attracted more and more attention. As the most commonly used detection method in non-destructive testing, magnetic flux leakage testing is widely used in steel, petroleum, petrochemical and other fields. It mainly detects defects such as corrosion, cracks, po...

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Application Information

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IPC IPC(8): G01N27/82G06N3/02G06N3/08
Inventor 韩文花吴正阳汪胜兵王建
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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