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 ( R 2 = 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.

中文翻译：

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基于AR最小熵反卷积结合小波谱峰态方法的硬木原木内部状态评估

这项研究的目的是探索使用声学信号分离和增强算法进行声学冲击测试的潜力，以评估硬木原木在内部衰减，空隙，裂纹和缺陷率方面的状况。通过声学冲击测试从四种硬木物种的15个原木中获得纵向声信号。缺陷分量从声学响应信号中分离出来，并基于自回归最小熵反卷积（AR-MED）方法得到增强，并从中导出峰度并用作评估原木内部条件的全局特征参数。与直接从原始信号获得的声速相比，峰度被认为是对数缺陷率的更有效预测器，具有更高的确定系数（R 2 = 0。89），并且不受原木种类的影响。为了识别缺陷的类型，提出了一种基于Morlet小波的复杂光谱峰度（SK）方法。研究结果表明，SK不仅可以确定类型，主要和次要主要缺陷，而且还可以识别那些无法通过整体声学参数检测到的缺陷。