Abstract
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.
Highlights
Some methods such as low-plasticity ball burnishing can improve the fatigue strength of steel components [1], but faults are frequent in metal pipelines because of long term usage or high pressure, so it is necessary to examine the pipelines regularly by nondestructive testing (NDT) methods
magnetic flux leakage (MFL) and magnetic Barkhausen noise (MBN) techniques may be ascribed to active magnetic test methods which require applying a strong artificial field to magnetize the tested objects
The MMM signals were detected along scanning lines by the MFL-4032A, a magnetic flux leakage/magnetic memory detector, which was a joint venture by Logistic Engineering University and Xiamen Eddysun Electronic Company
Summary
Some methods such as low-plasticity ball burnishing can improve the fatigue strength of steel components [1], but faults are frequent in metal pipelines because of long term usage or high pressure, so it is necessary to examine the pipelines regularly by nondestructive testing (NDT) methods. Among various NDT techniques, lots of nondestructive magnetic techniques have been extensively adopted to ensure the operating safety of ferromagnetic structures and components in engineering such as magnetic particle testing (MPT), eddy current testing (ECT), magnetic flux leakage (MFL), magnetic Barkhausen noise (MBN), magneto acoustic emission (MAE) and recently developed metal magnetic memory (MMM). These techniques utilize the inherent ferromagnetic properties of the steels for nondestructive evaluation of a wide range of material mechanical properties [3]. The forecasting fitting functions of the sizes of pipeline cracks are obtained
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