期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 69, 期 1, 页码 241-248出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2893009
关键词
Support vector machines; Aluminum; Surface cracks; Probes; Testing; Current measurement; Eddy currents; Aluminum; cracks; eddy current; eddy current testing (ECT); multifrequency excitation; noise excitation; support vector machine (SVM)
资金
- Instituto de Telecomunicacoes through the Portuguese Science and Technology Foundation [UID/EEA/50008/2019]
This paper presents a nonconventional excitation method with white noise to detect surface and subsurface cracks in aluminum plates, and the performance of the method was compared with the multifrequency excitation method. In a second stage, the best excitation method was combined with the machine learning algorithm support vector machine (SVM) to classify the location and depth of subsurface cracks in multilayer aluminum structures. The experimental measurements were performed on two stacked aluminum plates with their thickness equal to 4 and 3 mm. Several experimental tests were performed for the classification of the subsurface crack location, as well as the depth classification of the cracks. The selected features to train and test the SVM algorithm for classification are reported in this paper. The results obtained from the SVM approach include a classification obtained by training 72 of the experimental measurement data with linear, quadratic, polynomial, and Gaussian radial basis kernels and by testing the remaining 28 of the collecting measurement data.
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