An improved support vector regression method for quantifying magneticleakage defects in three-axis pipeline
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摘要: 为了提高管道漏磁内检测缺陷量化技术的精度，基于三轴漏磁内检测器采集到的缺陷漏磁数据，设计了一系列针对管道轴向、径向以及周向的特征提取方法，为后续进行缺陷的高精度量化提供了数据基础。针对缺陷不同尺寸量化任务下特征冗余的问题，基于近邻成分分析提出一种特征选择方法，该方法能够有效地剔除原始特征集中的无关特征。在基于支持向量回归的漏磁缺陷尺寸量化中，结合改进蝙蝠算法对支持向量回归的参数进行寻优，结果表明，所设计的量化方法能够有效降低时间复杂度，在一定程度上提高缺陷量化的准确性。Abstract: In order to improve the accuracy of the defect quantification technology for pipeline magnetic flux leakage internal detection, a series of feature extraction methods for pipeline axial, radial and circumferential directions was designed based on the defect magnetic flux data collected by the three-axis magnetic flux leakage internal detector, providing a data basis for the high-precision quantification of subsequent defects. Aiming at the problem of feature redundancy under the task of quantifying defects with different sizes, this paper proposes a feature selection method based on nearest neighbor component analysis, which can effectively eliminate irrelevant features in the original feature set. In the quantization of magnetic flux leakage defects based on support vector regression, this paper combines the improved bat algorithm to optimize the parameters of the support vector regression. The results show that the designed quantization method can effectively reduce time complexity and improve the accuracy of defect quantification to a certain extent.
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