摘要
针对目前超声检测领域缺陷识别率不高的现状,构建基于3个BP网络和D-S证据理论的融合模型,将数据融合技术应用于超声缺陷分类中。针对非稳态超声缺陷回波的特点,分别选择离散小波变换、小波包变换及经验模式分解提取其特征值。实验结果表明,该方法在超声缺陷分类的应用中是有效的,缺陷识别的准确率可达96%。
According to the state that low reliability of flaws identification at present, this paper put forward a fusion model combining by three BP networks and D-S evidence theory and applied data fusion technology into the field of ultrasonic flaws identification. Aimed at the characteristics of non-stationary ultrasonic echo-signals of flaws, wavelet packet transform, empirical module decomposition and wavelet transform were applied to feature extraction. Experiment shows that the method mentioned above has the power of effective identification, and measurement accuracy could reach 96%.
出处
《兵工自动化》
2011年第9期72-76,共5页
Ordnance Industry Automation
关键词
超声缺陷识别
BP网络
D-S证据理论
特征提取
ultrasonic identification of flaws
BP network
D-S evidence theory
feature extraction
