Wood Structure Nondestructive Detection Based on Singular Spectrum Analysis and SVM

Abstract

Abstract: In view of unknown defect of wood component defect, a method of wood structure nondestructive recognition based on Singular spectrum analysis and SVM was proposed. The wood specimen was tested to obtain the test signal by ultrasonic testing instrument. In order to eliminate the testing effect of the tester gain control and defect size, angle variation on the test defect echo amplitude, the abnormal fluctuation was to filter and characteristic of the original signal was extracted by singular spectrum analysis, and SVM could train the parameters and distinguish the wood defects. Simulation results show that the proposed method can distinguish standard samples and glue joint with accuracy of specimen for 97.5%, and recognition of knots specimens also reaches 95% with high accuracy.

Key words: defect recognition, singular spectrum analysis, SVM, PSO

CLC Number:

TP229

Zhou Guoxiong, Chen Aibin, Zhou Xianyan. Wood Structure Nondestructive Detection Based on Singular Spectrum Analysis and SVM[J]. Journal of System Simulation, 2016, 28(8): 1863-1869.

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