Defect magnetic field leakage side-view analysis Waveform size change (other factors remain unchanged) to see the change of the signal characteristics of the magnetic field leakage defect, and then comprehensively change the defect size to see the characteristic change of the magnetic field leakage signal signal. When there are a large number of feature quantities, feature quantity correlation analysis and feature quantity compression are also needed to simplify analysis calculation and dimensionality reduction processing, and improve the accuracy of defect outline size recognition. According to the theory of defect leakage magnetic field and a large number of experiments, the characteristic value of the length of the leakage magnetic field of the defect is the valley value LXP-P of the axial signal waveform of the flaw leakage magnetic field, the area of ​​the axial signal waveform and the peak value of the axial signal waveform Compared with LSa / LYP-P, the differential peaks and valleys of the axial signal waveform are longer than LDXP-P. These three feature quantities are used as components of the sample feature vector.
The characteristic value of the leakage magnetic field of the depth of the defect adopts the peak-valley value of the axial leakage magnetic field waveform signal of the flaw leakage magnetic field LYP-P, the area / valley ratio of the axial leakage magnetic field waveform signal LSa / LXP-P, the peak of the axial differential signal Valley length LDXP-P, the ratio of the defect width W after evaluation to the defect length L after evaluation, these four feature quantities are used as components of the sample feature vector.
Defect shape parameters (length, width, depth) are determined by multiple feature quantities, and there is a certain relationship between multiple feature quantities. Multiple nonlinear regression methods can be used to solve this problem. The specific process is to return the feature quantity related to the defect parameter and the defect size to a certain type of function, which is usually a nonlinear relationship. If the curve type of the actual problem is not easy to judge, polynomials can be used for approximation.
Through variable transformation, the nonlinear relationship is converted into a linear relationship to find an approximate solution. To this end, the type of function can be determined based on theory, experience, experimental data / scatter plot 0, and then transformed into a linear regression problem to solve.
in conclusion
On the outer surface of the pipeline with an outer diameter of 273mm and a thickness of 14.3mm, 140 defects of different sizes of circular and elliptical types were artificially processed to simulate corrosion defects for the detection experiment. 60 coefficients were selected to determine the evaluation model, and the remaining To verify, the length, width, and depth of the defect shape are evaluated by the feature length, width, and deep flux leakage signal characteristics selected in the article and the above comprehensive evaluation method.
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