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.
Highlights
Guided ultrasonic waves are widely used and have shown great potential in non-destructive evaluations (NDE) and structural health monitoring (SHM) systems
This paper presents a Lamb wave-based damage quantification method using a least squares support vector machine (LS-SVM) and genetic algorithm (GA) for metallic materials
The comparison of prediction data based on second-order multivariate model and GA based based least square support vector machine (LS-SVM). (a) T1; (b) T2; (c) T3
Summary
Guided ultrasonic waves are widely used and have shown great potential in non-destructive evaluations (NDE) and structural health monitoring (SHM) systems. The one-class support vector machine (SVM) was used to perform automatic anomaly detection and damage classification using various features from sensor readings Both ANN and SVM were used to locate the potential damage site in metallic plates in reference [23]. A number of studies have been reported to perform Lamb wave-based damage evaluation using data-driven methods, most of them focus on damage identification and classification. This paper presents a Lamb wave-based damage quantification method using a least squares support vector machine (LS-SVM) and genetic algorithm (GA) for metallic materials. In order to further investigate the accuracy and robustness of the proposed damage quantification method, fatigue testing with naturally developed cracks on lap joint components is performed. Lap-joint specimens are made from two different manufacturers with the same material and geometry and both the constant loading and variable loading case for fatigue testing are included, in order to verify the robustness of the proposed method
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