Abstract
Ferromagnetic materials are widely used in many fields of national economy. In actual engineering, under the influence of stress or environment, ferromagnetic materials can be defective and have serious consequences. Therefore, magnetic flux leakage inversion, which is speculating defects information according to the detected magnetic leakage signals, is of great practical significance. In allusion to the identification of irregular defects, this paper presented an inversion method based on singular value decomposition of magnetic dipole forward model, which is very effective in identifying irregular defects. This paper contrasted and analyzed the distribution characteristics of magnetic intensity horizontal component Mx when there was no defect and irregular defect, and the comparison verified that the magnetic intensity horizontal component Mx could be used as an inversion gist. Then this paper presented the magnetic dipole forward model B=LM. On account of the magnetic intensity component M containing defects information, this paper adopted the arithmetic of singular value decomposition of coefficient matrix L to solve the inversion equation LM=B and then acquired the distribution of magnetic intensity component M. In the end, this paper verified the validity of this method.
Highlights
Ferromagnetic materials are widely used in various fields
Considering that the neural network method is highly dependent on samples and the finite element neural network method requires a large computational capacity, this paper presents a new magnetic flux leakage detection inversion method based on the singular value decomposition of magnetic dipole forward model
This paper compared and analyzed the distribution characteristics of magnetic intensity horizontal component Mx firstly, and the comparison verified the validity of selecting Mx as inversion gist
Summary
Ferromagnetic materials are widely used in various fields. the economic losses caused by the defects of ferromagnetic materials are numerous every year. Magnetic flux leakage detection[1,2] has been widely used in defect detection of ferromagnetic materials due to its advantages such as high sensitivity, low cost, fast speed, low requirements on workpiece surface cleanliness. Considering that the neural network method is highly dependent on samples and the finite element neural network method requires a large computational capacity, this paper presents a new magnetic flux leakage detection inversion method based on the singular value decomposition of magnetic dipole forward model. The defect information is represented by magnetized intensity component, and the validity of this method is verified by different shape defects This inversion method does not require prior information and does not need to consider the problem of convergence. It is suitable for defects of various shapes and strong generalization ability is its prominent advantage
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