REVIEW ARTICLE
The Quantitative Prediction of Pipeline Cracks Using Metal Magnetic Memory Based on a Regression Model
Lihong Gong*, 1, 2, Zhuxin Li1, Zhiqiang Song1
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 1
Last Page: 8
Publisher Id: TOMEJ-8-1
DOI: 10.2174/1874155X01408010001
Article History:
Received Date: 14/11/2013Revision Received Date: 30/01/2014
Acceptance Date: 31/01/2014
Electronic publication date: 21/2/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
