Abstract:

Nondestructive testing(NDT) for wood inner-defect detecting, combined with wood sciences, electronics, signal procurement and processing, and pattern diagnosing, are very important for timber production, wood processing, evaluation of standing trees and assessment of wooden structures. This paper carried out the indoor experiments for NDT of Elm wooden test samples using ultrasonic instrument in order to identify the inner-defect patterns. Wavelet transform and wavelet packet analysis was employed to identify the characteristic values of defect signals. The original signals were decomposed, and then the energy varieties of traveling signals for different layers were calculated. The energy spectrum variety of ultrasonic signals at layer 5 were taken as the eigenvalues of transform matrix. The results of test showed that :1) The energy spectrum changes of a ultrasonic signal is proportional to the degree of defects in wood; 2) Energy spectrum changes at crunode 32 of layer 5 are the mostly significant compared with those at other crunodes; 3) Taking the energy varieties of signals at crunode 32 of layer 5 and the (5,0) crunode's wavelet radix as the character inputs of the artificial neural network respectively, the latter network for identifying the defect patterns works more efficiently than the former one with accuracy rate over 90%.

Key words: wood inner-defect detecting, ultrasonic testing, wavelet analysis, artificial neural network