Abstract
The primary bottleneck to extracting wood defects during ultrasonic testing is the accuracy of identifying the wood defects. The wavelet energy moment was used to extract defect features of artificial wood holes drilled into 120 elm samples that differed in the number of holes to verify the validity of the method. Wavelet energy moment can reflect the distribution of energy along the time axis and the amount of energy in each frequency band, which can effectively extract the energy distribution characteristics of signals in each frequency band; therefore, wavelet energy moment can replace the wavelet frequency band energy and constitute wood defect feature vectors. A principal component analysis was used to normalize and reduce the dimension of the feature vectors. A total of 16 principal component features were then obtained, which can effectively extract the defect features of the different number of holes in the elm samples.
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Bamford M, Batsale JC, Fudym O (2009) Nodal and modal strategies for longitudinal thermal diffusivity profile estimation: application to the non destructive evaluation of SiC/SiC composites under uniaxial tensile tests. Infrared Phys Technol 52:1–13
Oh JK, Kim CK, Hong JP, Lee JJ (2015) Improvement of robustness in ultrasonic attenuation spectroscopy for detecting internal insect damage in wood member of cultural heritage. J Wood Sci 61(2):136–142
Qi W, Wang LH (2006) Identifying the patterns of defects in timber using ultrasonic test based on wavelet neural networks. Sci Silvae Sin 42(8):63–68
Sfarra S, Theodorakeas P, Avdelidis NP, Koui M (2013) Thermographic, ultrasonic and optical methods: a new dimension in veneered wood diagnostics. Russ J Nondestr Test 49(4):234–250
Sun JP, Wang FH, Zhu XD (2008) Application of wavelet-neural network in defect location non-destructive testing of MDF. Chin J Sci Instrum 29(05):955–958
Sun JP, Hu YC, Wang FH (2013) Study on quantitative nondestructive test of wood defects based on intelligent technology. Chin J Sci Instrum 34(09):1955–1960
Wang EC, Qiu XC (2011) New method of lumber recognition using improved C-V model and wavelet transform. Comput Eng Appl 47(8):211–214
Wang LH, Yang HM (2007) Application of wavelet packet analysis and BP ANN in diagnosing the hole defects in Acer mono wood using ultrasonic quantitative testing. J Beijing For Univ 29(02):128–132
Wang FH, Zhu X, Sun JP (2004) Applications of wavelet analysis in the nondestructive test of medium density fiberboard. Sci Silvae Sin 42(10):91–94
Wang BX, Liu WF, Liu JN, Cui YY, Luo XZ (2012) Feature based method for classifying and detecting ultrasonic signals. J Tsinghua Univ 52(7):941–945
Yang HM, Yu L, Wang LH (2015) Effect of moisture content on the ultrasonic acoustic properties of wood. J For Res 26(3):753–757
Yu GX, Zhang AZ, Shi BZ (2007) Detection of timber decay by stress wave frequency spectrum. J Northeast For Univ 35(10):22–25
Zhang YR, Fu L, Zhou XQ (2013) Near infrared detection of wheat water content based on bp neural network. J Henan Univ Technol 34(1):17–20
Zhao D, Zhu HJ (2010) Study on acoustic emission of wood defects based on wavelet packet analysis. Comput Eng Appl 46(11):220–222
Zhou JM, Sun K, Li B, Li P, Xu QY (2015) Simulation and Localization of Ultrasonic Defect Detection Based on Array Probe. J East China Jiaotong Univ 32(5):105–109
Zhu HJ (2007) Acoustic emission testing for wood materials based on wavelet neural network. In: Dissertation of Beijing Forestry University, Beijing: Beijing Forestry University
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Project funding: This study was financially supported by the Fundamental Research Funds for the Central Universities (2572016CB11 and 2572014CB35), Natural Science Foundation of Heilongjiang Province (F2015036 and QC2014C010) and 948 Project (2014-4-78).
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Corresponding editor: Hu Yanbo
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Yang, H., Yu, L. Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. J. For. Res. 28, 395–402 (2017). https://doi.org/10.1007/s11676-016-0297-z
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DOI: https://doi.org/10.1007/s11676-016-0297-z