油气管道漏磁数据处理和缺陷识别量化方法的研究进展

Progress in Research on Magnetic Flux Leakage Data Processing and Defect Identification Quantification Methods

漏磁法是最稳定的无损在线检测技术之一,用于评估油气管道的健康状况。从通道基线校正、异常值判别、数据缺口的恢复和滤波四个方面阐述了漏磁数据预处理的方法和步骤;结合漏磁数据缺陷的峰谷值等显性特征,总结了不同特征提取方法下缺陷信号的本质特征;介绍了基于支持向量机、神经网络、图像处理和形态学等方法构建缺陷反演模型。最后,从数据预处理和缺陷识别量化两方面展望了漏磁信号处理未来的研究方向。

Magnetic flux leakage(MFL)method is one of the most stable nondestructive on-line testing techniques used to assess the health of oil and gas pipelines.The methods and steps of MFL data pre-processing are described from four aspects:channel baseline correction,outlier discrimination,data gap recovery and filtering.According to the dominant characteristics such as the peak-valley value of the defect in MFL data,the essential characteristics of the defect signal under different feature extraction methods are summarized.The defect inversion models based on support vector machine,neural network,image processing and morphology are introduced.Finally,the future research directions of MFL signal processing was prospected from two aspects:data pre-processing and defect identification quantification.