基于改进信赖域算法的三维不规则缺陷重构

漏磁检测广泛应用于铁磁性材料设备的在线检测当中,是一种有效的缺陷检测方法。如何利用缺陷漏磁信号进行三维不规则缺陷轮廓重构是漏磁检测中的关键问题。然而,三维不规则缺陷漏磁检测的有限元模型计算量大,因此难以快速获得精确的漏磁信号,并且由于缺陷重构的不适定性,研究中不容易获得不规则缺陷的精确轮廓。本文提出了一种用于计算三维不规则缺陷漏磁信号的单元磁偶极带叠加模型,并验证了使用该正演模型进行漏磁计算的有效性,针对三维缺陷轮廓重构的高维优化问题,提出了一种带边界约束的基于信赖域的投影Levenberg-Marquart算法,实现了三维不规则缺陷轮廓的重构。实验结果表明:该三维不规则缺陷重构方法不仅不需要大量的漏磁检测数据,并且相对于群智能算法,重构误差降低了90.1%,最大深度误差降低了53.9%,耗费时间减少了96.1%,实现了高精度的缺陷重构。

Reconstruction of three-dimensional irregular defects based on improved trust region algorithm

The magnetic flux leakage testing (MFL) is an effective defect detection method which is widely used in the on-line detection of ferromagnetic materials. How to use the magnetic flux leakage signal to reconstruct the three-dimensional irregular defect profile is a key problem in magnetic flux leakage detection. However, the finite element model of three-dimensional magnetic flux leakage detection of irregular defects requires a large amount of calculation. Therefore, it is difficult to obtain accurate magnetic flux leakage signals quickly. Moreover, due to the inadequacy of defect reconstruction, it is difficult to achieve accurate profiles of irregular defects in the study. In this paper, a unit magnetic dipole band superposition model is proposed for computing magnetic flux leakage signals of threedimensional irregular defects. The effectiveness of the forward model for magnetic flux leakage calculation is verified. For the highdimensional optimization problem of three-dimensional defect profile reconstruction, a trust region-based projection Levenberg-Marquart algorithm with boundary constraints is proposed. The contour reconstruction of three-dimensional irregular defects is realized. Experimental results show that this method does not need a lot of MFL detection data. Compared with the swarm intelligence algorithm, the reconstruction error is reduced by 90. 1%, the maximum depth error is reduced by 53. 9%, and the time consumption is reduced by 96. 1%, thus realizing the high-precision defect reconstruction.