Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

The aim of this research was to explore the potential of acoustic impact test to evaluate the condition of hardwood logs in regard to internal decay, void, crack and defect ratio using an acoustic signal separation and enhancement algorithm. Longitudinal acoustic signals were obtained from 15 logs of four hardwood species through acoustic impact testing. The defect components were separated from the acoustic response signals and enhanced based on the autoregressive minimum entropy deconvolution (AR-MED) method, and from which the kurtosis was derived and used as the global feature parameter for evaluating the internal condition of logs. Compared with the acoustic velocity obtained directly from the original signal, the kurtosis was deemed to be a more powerful predictor of log defect ratio with higher coefficient of determination (R² = 0.89) and was not affected by log species. To identify the type of defects, a complex Morlet wavelet-based spectral kurtosis (SK) method was proposed. The research results indicated that the SK can not only determine the type and primary and secondary major defects, but also be able to identify those that were not detectable by global acoustic parameters.