摘要
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis （WPA） was applied to feature extraction of ultrasonic signal, and optimal Support vector machine （SVM） was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2008年第5期908-913,共6页
Chinese Journal of Scientific Instrument
基金
Supported by the Research Program of International Technology Collaboration and Communication of Sichuan(2007H12-017)
关键词
裂纹鉴别技术
超声波
转轴
支持向量机
ultrasonic testing
wavelet packet analysis
support vector machine
flaw identification
