Signal Characteristic Extractio of Wood Defects Based on Wavelet Packet
The wavelet packet decomposition was made for the ultrasonic testing signal of wood defects. The wavelet function of Db5 was applied to make the three-layer wavelet packet decomposition for the wood defects. Fonr characteristic parameters of wave formBx, wave crest Bf, energy distributionEf and energy percentage E were extracted in the nodes of Layer 3. The effective evaluation standard was established on the basis of characteristic information extraction. The separability of different defects in time domain eigenvector and frequency domain eigenvector was compared and analyzed respectively. The frequency domain eigenvector with better separability was served as the recognition eigenvalue of classifying defects sorts. BP neural network was used to identify the extracted frequency domain eigenvector, and the total recognition rate reached to 83.3％. Therefore, the method presented in the study is feasible in the wood defects recognition
ultrasonic testing wavelet packet transform feature extraction BP neural network
Huimin Yang Lihai Wang Yumei Wang
Northeast Forestry University Key Laboratory of Forest Sustainable Management and Environmental Micr Harbin Normal University Heilongjiang Province, China
国际会议
哈尔滨
英文
886-889
2011-08-12（万方平台首次上网日期，不代表论文的发表时间）