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Materials 2017, 10(6), 648; doi:10.3390/ma10060648

Lamb Wave Damage Quantification Using GA-Based LS-SVM

1
Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing 100191, China
2
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
3
Siemens Corporation, Corporate Technology, 755 College Rd. E., Princeton, NJ 08540, USA
*
Author to whom correspondence should be addressed.
Received: 30 April 2017 / Revised: 8 June 2017 / Accepted: 9 June 2017 / Published: 12 June 2017
(This article belongs to the Special Issue Structural Health Monitoring for Aerospace Applications 2017)

Abstract

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. View Full-Text
Keywords: Lamb wave; GA-based LS-SVM; damage quantification; fatigue crack Lamb wave; GA-based LS-SVM; damage quantification; fatigue crack
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, F.; Wang, N.; He, J.; Guan, X.; Yang, J. Lamb Wave Damage Quantification Using GA-Based LS-SVM. Materials 2017, 10, 648.

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