Application Research for Ultrasonic Flaw Identification Based on Support Vector Machine
Jing Huang, Binglei Guan
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.206How to use a DOI?
- Ultrasonic Testing; Flaw Identification; Support Vector Machine (SVM); Pattern Classification
- Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of pipelines. A novel automatic identification approach of flaws using support vector machine (SVM) is presented. Wavelet transform is applied to feature extraction of ultrasonic echo signals, and SVM is to perform the identification task. To validate this approach, some experiments are performed. The results show that unlike conventional and artificial neural networks (ANN) identification methods the new technique performs better than conventional evaluation ones with advantages of high identification performance for pipeline flaws, lower cost, excellent generalization.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jing Huang AU - Binglei Guan PY - 2015/12 DA - 2015/12 TI - Application Research for Ultrasonic Flaw Identification Based on Support Vector Machine BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SP - 986 EP - 990 SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.206 DO - https://doi.org/10.2991/icmmcce-15.2015.206 ID - Huang2015/12 ER -