针对石油套管缺陷超声无损检测(NDT)中缺陷回波的特点，提出了一种基于小波包分解和支持向量机(SVM)的缺陷智能识别新方法。分析了Gabor、小波和小波包3种信号时频变换分解方法的特点，并进行了基于3种方法生成的特征数据可分性比较，确定了小波包分解方法效果最好。根据SVM解决分类问题的原理，采用SVM法对3种时频分解提取的缺陷信号特征数据进行识别。试验表明，基于小波包分解局部熵的特征提取结合SVM模式智能识别的组合方法，可应用于石油套管上的4种典型缺陷的识别。
A new intelligent flaws identification method was presented base on wavelet packet decomposition and support vector machine (SVM), according to the characteristics of ultrasonic nondestructive testing (NDT) echo signals. The characteristics of Gabor transform, wavelet transform, and wavelet packet transform in signal decomposing were discussed. The separability of features achieved by three methods above was compared, and the wavelet packet method was proved to be the best. The classification principle of SVM method was introduced. And it was adapted to identify the features achieved by three time-frequency decomposing methods. The features extraction method with the SVM algorithm was proved to be efficient to identify four typical flaws in oil casing pipe.
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