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Stress wave evaluation by accelerometer and acoustic emission sensor for thermally modified wood classification using three types of neural networks

Authors
  • Nasir, Vahid1
  • Nourian, Sepideh1
  • Avramidis, Stavros1
  • Cool, Julie1
  • 1 The University of British Columbia, Department of Wood Science, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada , Vancouver (Canada)
Type
Published Article
Journal
European Journal of Wood and Wood Products
Publisher
Springer Berlin Heidelberg
Publication Date
Nov 22, 2018
Volume
77
Issue
1
Pages
45–55
Identifiers
DOI: 10.1007/s00107-018-1373-1
Source
Springer Nature
License
Yellow

Abstract

Classification of thermally modified wood (TMW) allowing the distinction between different processing temperatures and the corresponding changes in wood properties is a crucial task in TMW grading. In this study, stress wave evaluation technique was used to classify the heat treatment level. Accordingly, an acoustic emission (AE) sensor and a pair of accelerometers captured stress waves generated by pendulum impact, and the data was used to classify the heat treatment level of thermally modified Western hemlock wood samples. Sensory features were extracted from time, frequency, and wavelet domain analysis. The extracted features were then used to train multilayer perceptron (MLP), group method of data handling (GMDH), and linear vector quantization (LVQ) neural networks for TMW classification. The results showed that while the features extracted from the accelerometers such as stress wave velocity and wood dynamic modulus of elasticity showed poor classification performance, acoustic emission sensory features were effective for classification of TMW. Wavelet domain features lead to better classification than those extracted from time and frequency domains. Feature fusion approach comprising the features from all the signal domains showed the best classification performance that was further improved by using a dimensionality reduction approach. The linear discriminant analysis was conducted on all acoustic emission features and resulted in 91.1% and 89.1% accuracy obtained from the LVQ and GMDH network, respectively. This performance was further increased to 98% and 97% using the LVQ and GMDH models when the input was combined with wood moisture content. The MLP neural network did not seem as suitable as the other two models. Neural network modeling using the captured stress wave data from an AE sensor could therefore be a promising nondestructive evaluation method for TMW classification.

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