Three-dimensional pipeline flux leakage imaging defect quantizing method

Abstract

The invention discloses a three-dimensional pipeline flux leakage imaging defect quantizing method which includes the steps: performing defect width quantization by taking circumferential flux leakage signal characteristics as evaluation criteria to obtain a defect width quantization formula; performing defect length quantization by taking radial flux leakage signal characteristics as evaluation to obtain a defect length quantization formula; performing defect depth quantization by taking axial flux leakage signal characteristics and the radial flux leakage signal characteristics as comprehensive evaluation to obtain a defect depth quantization formula; acquiring axial, radial and circumferential discrete three-dimensional flux leakage detection data of a detected component, filtering the axial, radial and circumferential discrete three-dimensional flux leakage detection data to extract defect flux leakage signal characteristic values, and substituting the characteristic values into the formulas to obtain the sizes and distribution situations of defects. Multiple flux leakage signal characteristics are comprehensively and collectively evaluated, the method is high in distinguishing degree and accurate, simple and easy in calculation, and the identification ability and the quantization accuracy of various irregular defects and applicability are improved.

Description

technical field [0001] The invention relates to the technical field of non-destructive testing, in particular to a defect quantification method for three-dimensional magnetic flux leakage imaging of pipelines. Background technique [0002] Magnetic flux leakage testing is the most widely used non-destructive testing method, especially in the quality testing and safety monitoring of ferromagnetic materials, which has incomparable advantages. However, due to the complex nonlinear relationship between the detection signal and the defect size, it has become a technical problem to analyze the magnetic flux leakage detection signal to realize the characteristic identification and quantitative evaluation of defects. In addition, traditional defect quantification methods mostly use the signal characteristics of axial magnetic flux leakage detection as the evaluation. The source of signal characteristics is single, and the recognition degree is not high, which leads to the decline of...

AI Technical Summary

Problems solved by technology

However, due to the complex nonlinear relationship between the detection signal and the defect size, it has become a technical problem to analyze the magnetic flux leakage detection signal to realize the feature identification and quantitative evaluation of defects.

In addition, traditional defect quantification methods mostly use the signal characteristics of axial magnetic flux leakage detection as the evaluation, the source of signal characteristics is single, and the recognition degree is not high, which leads to the decline of recognition ability and quantification accuracy

[0003] In related technologies, for example, the analysis method of magnetic flux leakage detection data for pipeline defects can improve the analysis efficiency and quantitative analysis accuracy of various steel pipeline defect detection signals, but by displaying the cloud image of the data, it can only be qualitatively judged whether there are defects in the pipeline. Defect quantification cannot be realized; for example, the identification method of pipeline corrosion defect type, although a large amount of magnetic flux leakage detection data can be quickly classified and processed through simple algorithms and international standards, but it can achieve better recognition accuracy for regularized defects; for example, based on three-dimensional finite element The defect identification and quantitative evaluation method of the neural network, although the initial estimated value of the characteristic parameters of the defect is given and sent to the three-dimensional finite element neural network for iterative calculation, the defect is realized by comparing the error between the calculated value of the defect leakage magnetic field and the measured value. Identification and quantitative evaluation, so that it has a certain ability to identify and quantify defects, but the algorithm is more complicated, the calculation time is long, and the efficiency is not high

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