The Quantitative Prediction of Pipeline Cracks Using Metal Magnetic Memory Based on a Regression Model

The technique of metal magnetic memory (MMM) has great advantages in detecting early failures such as stress concentration and fatigue damage of ferromagnetic components, which has been widely applied due to its high efficiency, low requirements for surface preparation and ease of operation. However, research into the quantitative description of defect characteristics is still far from adequate. To promote relative study in this area, in this paper, a regression model is employed to analyze the sizes of surface cracks in pipelines. Three nonlinear functions are obtained to predict the length, width and depth of cracks respectively based on a regression model. Length prediction is convenient and accurate, with the average coefficient of determination of training samples up to 0.994 and that of testing samples 0.962. Moreover, as the width and depth are small (less than 2 mm), the accuracy of size prediction is very high. The obtained functions provide a useful method of predicting the crack sizes of pipelines according to MMM signals.