REVIEW ARTICLE
Diagnosis Model of Pipeline Cracks According to Metal Magnetic Memory Signals Based on Adaptive Genetic Algorithm and Support Vector Machine
Lihong Gong*, 1, 2, Zhuxin Li1, Zhen Zhang1
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 1076
Last Page: 1080
Publisher Id: TOMEJ-9-1076
DOI: 10.2174/1874155X01509011076
Article History:
Received Date: 17/02/2014Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 2/11/2015
Collection year: 2015
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
Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel. The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling them to be located more accurately.
