谱熵分析方法在TOFD信号特征提取中的应用
Feature Extraction of TOFD Signal Based on Spectrum Entropy Analysis Method
摘 要
超声TOFD检测技术在厚大焊缝的检测方面具有较强优势,但其检测结果的定性识别目前还依赖人员完成,而自动识别技术能够降低人为因素对检测结果分析的干扰。但若欲实现检测结果的自动识别,特征量提取是关键。通过控制焊接规范,制备了含有气孔、夹渣、裂纹、未焊透和未熔合缺陷的试件,分析了各类缺陷的TOFD信号频域二维信息熵特征,即谱熵和谱的重心频率。结果表明,该二维信息熵可将五类缺陷信号分开,为缺陷的自动识别提供有效的特征量。
缺陷分类
信号特征
二维信息熵
Ultrasonic TOFD
Defect classification
Signal feature
Two-dimensional entropy
Abstract
Ultrasonic TOFD method takes advantages in testing thick weld, but the qualitative recognition of testing results now still relies on inspectors, therefore the testing results estimation is affected by personnel experience. By contrast, automatic recognition technology can reduce human factors impact on the testing results analysis. However, in order to implement the automatic recognition, feature extraction is the key point. In this paper, by controlling the welding specification, the specimens with porosity, slag, cracks, incomplete fusion and lack of penetration defect were prepared. Then two-dimensional entropy features in frequency domain of TOFD signal, which were spectral entropy and center frequency of spectral, for above mentioned kinds of defects were analyzed. The results show that the two-dimensional entropy in frequency domain can separate the five types of defect signal. It could provide effective characteristics for automatic identification of defect.
中图分类号 TG115.28
所属栏目 试验研究
基金项目
收稿日期 2014/4/15
修改稿日期
网络出版日期
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备注杨双羊(1980-),男,硕士,主要从事海洋平台焊接结构建造及质量控制工作。
引用该论文: YANG Shuang-yang,SHENG Zhao-yang,LU Yan,GAO Chen. Feature Extraction of TOFD Signal Based on Spectrum Entropy Analysis Method[J]. Nondestructive Testing, 2014, 36(11): 45～48
杨双羊,盛朝阳,路燕,高晨. 谱熵分析方法在TOFD信号特征提取中的应用[J]. 无损检测, 2014, 36(11): 45～48
被引情况:
【1】方正中,赵烨,杨克已,郑鼎鼎,周红民, "基于小波匹配追踪的薄板焊缝超声TOFD稀疏化成像方法",无损检测 37, 51-55(2015)
【2】孟贵云,张世宏,陈振华,李新蕾,王宏, "超声TOFD检测声波在不锈钢焊缝中的传播特性及其应用",无损检测 37, 51-56(2015)
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参考文献
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【3】SHITOLE, C S N, ZZHRAN O, AL-NUAIMY W, et al. Combining fuzzy logic and neural networks in classification of weld defects using ultrasonic time-of-flight diffraction[J]. Insight: Non-Destructive Testing and Condition Monitoring, 2007, 49(2): 79-82.
【4】MOURA E P, SILVA R R, SIQUEIRA M H, et al. Pattern recognition of weld defects in preprocessed TOFD signals using linear classifiers[J]. Journal of Nondestructive Evaluation, 2004, 23(4): 163-172.
【5】ZAHRAN O, AL-NUAIMY W. Automatic data processing and defect detection in time-of-flight diffraction images using statistical techniques[J]. Insight, 2005, 47(9): 538-542.
【6】LAWSON S W ,PARKER G A. Automatic detection of defects in industrial ultrasound images using a neural network[J]. Proceedings of SPIE, 1996 (2786): 37-47.
【7】KECHIDA A, DRAI R, KHELIL M. 2D Gabor functions and FCMI algorithm for flaws detection in ultrasonic images[J]. Proceedings of World Academy of Science Engineering and Technology. 2005(9): 184-188
【8】CHARLESWORTH J P, TEMPLE J A G, ZIPIN R B. Engineering Applications of Ultrasonic Time-of-flight Diffraction[M]. Philadelphia, USA: Research Studies Press Ltd., 2001.
【9】刘红星, 左洪福, 姜澄宇,等. 信号频谱的二维向量及其应用[J]. 中国机械工程,1999,10(5):537-539.
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