Three-dimensional magnetic flux leakage detection defect reconstruction method based on generative adversarial network

Abstract

The invention discloses a three-dimensional magnetic flux leakage detection defect reconstruction method based on a generative adversarial network, and belongs to the technical field of nondestructivetesting. The method is characterized by comprising the following steps: step 1, acquiring a defect image by using a depth camera to obtain defect data; 2, adopting a magnetic flux leakage detection instrument, and obtaining magnetic flux leakage data through post-processing of the magnetic flux leakage data; 3, dividing all data into sample data and test data, dividing the sample data into training data and verification data; 4, matching the magnetic flux leakage data in the sample data with defect data; 5, obtaining a GAN final model; and step 7, obtaining a defect three-dimensional contour.According to the three-dimensional magnetic flux leakage detection defect reconstruction method based on the generative adversarial network, three-dimensional reconstruction is carried out on the bottom plate by using the GAN model, defect three-dimensional contour reconstruction can be rapidly carried out, the reconstruction speed is high, the stability is good, the precision is high, the robustness is good, and the reconstruction speed and precision of various irregular defects are facilitated.

Description

technical field [0001] A three-dimensional magnetic flux leakage detection defect reconstruction method based on a generative confrontation network belongs to the technical field of nondestructive testing. Background technique [0002] Magnetic flux leakage testing is a commonly used non-destructive testing technology. Because of its simple principle, strong online detection capability and good detection effect, it is widely used in the quality testing and safety assessment of ferromagnetic materials such as pipelines, storage tank floors, and steel wire ropes. Due to the uncertainty of the shape of the defect and the complex nonlinear relationship between the magnetic flux leakage signal and the shape of the defect, it has become a hot and difficult issue in the research of magnetic flux leakage detection technology. Most of the existing defect reconstruction methods use the uniaxial magnetic flux leakage detection signal as the data source to perform two-dimensional magnet...

AI Technical Summary

Problems solved by technology

Most of the existing defect reconstruction methods use the uniaxial magnetic flux leakage detection signal as the data source to perform two-dimensional magnetic flux leakage reconstruction of a certain section, or realize three-dimensional reconstruction through interpolation at different detection points on a two-dimensional basis. The detection signal of the method is single, the calculation model is complex, the amount of calculation is large, and the reconstruction accuracy is low

The existing 3D defect reconstruction methods either have low reconstruction accuracy, or the calculation model is complex and the calculation time is long, which makes the reconstruction efficiency low

[0003] In the prior art, the Chinese patent application number 201310220995.8 discloses a two-dimensional reconstruction method of oil pipeline defect least squares support vector machine. The processed magnetic flux leakage signal data is used to reconstruct the contour of pipeline defects, but in this technique, because it only inverts the contour of a certain fault, it belongs to two-dimensional reconstruction, and its reconstruction accuracy is low

The Chinese patent with the application number 201510239389.X discloses a method and device for reconstructing the defect contour of three-dimensional magnetic flux leakage detection. Genetic algorithm and tabu search algorithm, but each iteration requires re-calculation of finite element, long calculation time, high calculation cost and low efficiency

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