超声检测信号多特征SVM-Bayes融合识别

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振动与冲击 ›› 2011, Vol. 30 ›› Issue (12) : 265-269.

论文

超声检测信号多特征SVM-Bayes融合识别

车红昆; 吕福在; 项占琴

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Ultrasonic signal recognition by multiple features svm-bayes fusion method

Che Hong-kun; Lu Fu-zai; Xiang Zhan-qin

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摘要

分析了超声检测信号识别中存在的问题。研究了将支持向量机和贝叶斯推理相结合的多特征融合识别算法。阐述了支持向量机解决分类问题的原理以及贝叶斯推理原理。设计了基于最大后验概率准则的多缺陷类型多特征SVM-Bayes融合识别方法。介绍了四种不同的特征提取方法。分别将单特征SVM方法和SVM-Bayes融合方法应用于石油套管缺陷检测信号的识别。对比试验表明：SVM-Bayes融合识别方法能有效识别上述缺陷信号，其在识别率和泛化性方面都比单特征的SVM识别方法有优势。

Abstract

Problems of signal recognition in ultrasonic inspection are analyzed. A new fusion recognition method base on multiple features extraction is researched, which compounds with support vector machine theory and Bayes reasoning. The principles of SVM method and the Bayes reasoning are introduced. Fusion recognition method base on maximum a posteriori(MAP) are designed to identify the signals of different flaws with features extracted from different ways. Four feature extraction methods from different spatial domains of a signal are presented for fusion recognition. Experiments with both SVM method and SVM-Bayes method are carried out to identify the flaw signals of oil casing pipe. The result shows that flaws can be identified effectively by SVM-Bayes method, and both recognition correct rate and generalization are better than a single feature SVM method.

导出引用

车红昆;吕福在;项占琴. 超声检测信号多特征SVM-Bayes融合识别[J]. 振动与冲击, 2011, 30(12): 265-269

Che Hong-kun;Lu Fu-zai;Xiang Zhan-qin. Ultrasonic signal recognition by multiple features svm-bayes fusion method[J]. Journal of Vibration and Shock, 2011, 30(12): 265-269