Numerical Simulation and Experiment on Magnetic Flux Leakage Inspection of Cracks in Steels
The magnetic flux leakage inspection method is widely use d in the detection of pipelines, storage tanks and other steel materials. Especially in c orrosion pits detection, it has become a matured technology. As cracks may exist in the steels which may lead to big hazards, its detection becomes very important. Meanwhile, the cracks evaluation by MFL inspection can be determined by many parameters, it has much difficulty t o implement due to its weaker signals compared with pits. In this research, the finit e element model for cracks’ MFL is established and corresponding method are carried out. Furthe rmore, parameters such as depth, width, angle of cracks and scanning direction are analyzed. Also, the experiments are done which show great reliability comparing with numerical s imulations. The results show that the cracks can not be detected when the scanning direction para llels with it. So the cross-scanning method is recommended for MFL inspection on cracks.
A comparative study of 3D and axisymmetric magnetizer assemblies used in magnetic flux leakage inspection of pipelines
Axisymmetric numerical codes are often used in modeling non-destructive testing phenomena, to approximate geometries and defects which are not truly axisymmetric in nature. This is especially true in cases where large computational resources are needed to model the exact geometry. However, there has been no study to confirm the nature and extent of errors introduced by an axisymmetric approximation. The gas pipeline industry makes use of bath axisymmetric and non-axisymmetric magnetizers for the magnetic flux leakage inspection of pipelines. The authors utilize this platform to perform a comparative analysis of axisymmetric and non-axisymmetric magnetizers from a design perspective and also study the differences in respective electromagnetic behavior.
Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.