Signal Detection of Welding Joint Flaw Based on Wavelet-Support Vector Machine

Abstract:

The friction welded joints made by GH4169 heat metal alloys are detected by U1traPAC system of the ultrasonic wave explore instrument. Aimed at the blemish signal characteristics, this article introduce Support Vector Machine (SVM) theory, which is based on statistical theory and structural risk minimization principle, to carry out multi-classification study of the detection signal. We decompose de-noising signals with wavelet packet transform, and extract energy eigenvalues according to "energy- defects". In accordance with designed "1-to-v" SVMs scheme, we respectively input normalized eigenvector to the SVM model to obtain the Forecast data. It is verificated that the limited existing data and information is well used by SVM and the signal is accurately been classificated. All of these verify that SVM has a strong generalization ability.

• Periodical: Advanced Materials Research (Volumes 1120-1121)
• Pages: 1385-1389
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1120-1121.1385
• Online since: July 2015
• Authors: Xin Yin, Yuan Peng Liu, Xian Zhang Feng
• Keywords: Support Vector Machine (SVM), Ultrasonic Testing, Wavelet Packet

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