Pattern Classification of Acoustic Emission Signals during Wood Drying by Principal Component Analysis and Artificial Neural Network

Abstract

This study was performed to classify the acoustic emission (AE) signal due to surface check and water movement of the flat-sawn boards of oak (Quercus Variablilis) during drying using the principle component analysis (PCA) and artificial neural network (ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count, event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 % of the variance of AE parameters could be accounted for by the first and second principal components. An ANN was successfully used to classify the AE signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.

An improved method and data analysis for ultrasound acoustic emissions and xylem vulnerability in conifer wood

“…UAEs from wood originate from cavitating xylem elements (Milburn and Johnson 1966, Tyree and Sperry 1989b) and are used as indicators of drought stress in living plants and to construct VCs. While it has been known or suspected that some UAEs do not stem from cavitations, as shown by the fact that the number of signals was almost two times higher than the number of tracheids in P. abies samples (Rosner et al 2006), and the amplitude or energy of individual UAEs are known to vary in a wide range (Beall 2002, Mayr and Rosner 2011), few attempts have been made to distinguish cavitations from other events (Kowalski et al 2004, Kim et al 2005).…”

A Novel Separation Technique of Flexural Loading-Induced Acoustic Emission Sources in Railway Prestressed Concrete Sleepers

“…Referring to [40], the correlation analysis was conducted to study the linear relationship between these signatures. The features that showed the most correlation were discarded from the further calculations to avoid the multi-collinearity between AE features.…”

Acoustic emission monitoring of wood materials and timber structures: A critical review

“…The advantages of the AE technique include the capability for nondestructive and in-situ evaluation of wood structures, assessing the position of the developing crack or damage, and monitoring the dynamic behavior of wood materials under loading. For instance, the AE has been used to assess the surface checks formation and water movement in wood during the drying process [44]. The crack tip propagation during wood machining has also been monitored using the cluster analysis of AE data [45].…”

“…Most research on AE monitoring of the wood drying process has focused on the signal in the time domain, and data-driven approaches such as machine learning have rarely been employed in wood drying monitoring. Kim et al [44] combined principal component analysis (PCA) and ANN for pattern classification of acoustic emission signals during wood drying. They could successfully differentiate the AE signals from those related to check formation due to moisture movement.…”