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The Magnetic Leakage Inversion Method Based on Singular Value Decomposition of Magnetic Dipole Forward Model

机译:基于磁偶极正向模型奇异值分解的漏磁反演方法

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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.
机译:铁磁材料广泛应用于国民经济的许多领域。在实际工程中,在应力或环境的影响下,铁磁材料可能会出现缺陷并造成严重后果。因此,根据检测出的漏磁信号推测缺陷信息的漏磁反转具有很大的现实意义。针对不规则缺陷的识别,提出了一种基于磁偶极正向模型奇异值分解的反演方法,对识别不规则缺陷非常有效。对没有缺陷和不规则缺陷的磁场强度水平分量Mx的分布特征进行了对比和分析,比较验证了磁场强度水平分量Mx可以作为反演依据。然后,本文提出了磁偶极子正向模型B = LM。由于包含缺陷信息的磁强度分量M,本文采用系数矩阵L的奇异值分解算法,求解了反演方程LM = B,得到了磁强度分量M的分布。最后,本文验证了该方法的有效性。

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