Application of wavelet packet analysis and BP ANN in diagnosing the hole defects in Acer mono wood using ultrasonic quantitative testing
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摘要: 为了实现对木材孔洞缺陷的定量检测,在室内常温下,用RSM-SY5非金属超声波检测仪对50个孔洞缺陷的色木试件进行透射检测.通过对超声检测信号的小波变换特征分析,得到32个从低频到高频的小波包系数,提取其各频带内信号的能量变化量,构造一个32维特征向量,作为BP神经网络的输入参数,最后将这些特征输入神经网络进行训练和识别.结果表明:色木孔洞大小的总识别率达到88%;网络仿真的输出结果和目标输出做线性回归分析,得到的相关系数在0.8~0.9之间,训练结果比较理想.Abstract: Under the normal circumstances in the laboratory,a series of testing for detecting and diagnosing,both qualitatively and quantitatively,and the defects of holes with different diameters in 50 maple wood samples using RSM-SYS ultrasonic instrument were carried out.The 32 coefficients of wavelet packet from low frequency to high frequency were obtained by wavelet packet analysis of eigenvalues derived from ultrasonic signals by using Matlab.The most important eigenvalue,energy variation sequences of a signal at different frequency bands,was extracted from the coefficients and a new 32 dimension eigenvector was composed of those eigenvalues,which can be used as the input matrix for the Back Propagation Artificial Neural Network(BP ANN) for learning,training and diagnosing.The results showed that the accuracy rate of detecting and diagnosing hole defects in A.mono wood was up to 88% and the index of correlation between simulation outputs and test objective outputs of BP ANN diagnosing system was ranging from 0.8 to 0.9.
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Keywords:
- Acer mono /
- hole defects /
- ultrasonic testing /
- wavelet packet analysis /
- BP ANN
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期刊类型引用(7)
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百度学术
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其他类型引用(2)
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