基于AR-MED的阔叶材原木声信号特征参数提取及原木质量分等

Feature extraction and quality grading method by virtue of acoustic signals generated from hardwood logs based on AR-MED

阔叶材原木的内部缺陷检测和质量分等是提高其利用率和经济效益的有效手段,然而因缺陷声信号的非平稳性和缺陷类型特征的重叠现象,有效的质量评估声参数非常有限。基于此,提出一种基于自回归(AR)和最小熵反褶积(MED)相结合的特征声参数提取与分等方法。基于赤池信息量准则(AIC)应用AR线性滤波器滤除声信号的周期平稳成分,对包含缺陷信息的残差信号进行MED增强,并将计算所得的峭度值作为表征声信号的特征参数,由峭度值对样本原木进行质量分等,并与传统的速度分等进行比较。数值仿真与阔叶材原木实测结果表明,该方法能够显著提高缺陷信号的峭度值并对原木质量进行有效地分等。

Internal defect detection and quality grading for hardwood logs could improve its utilization rate and economic benefit.Aiming at the insufficient acoustic parameters in quality assessment due to the non-stationary features of acoustic signals and the overlapping of defect features,a method for feature extraction and quality grading was proposed based on the autoregressive model (AR) and minimum entropy deconvolution (MED).An AR-based linear filter was applied to filter periodic deterministic components from original signals according to the Akaike Information Criterion (AIC).Then,the residual signal containing defect informations was enhanced by using the MED technique,and the kurtosis value of the enhanced signal was used as the characteristic parameter.The quality of sample logs was graded by the kurtosis,and the grading result was compared with that graded by velocity.Numerical simulations and measured results of the hardwood logs show that the proposed method can significantly increase the kurtosis values of defect signals and efficiently classify the hardwood logs in quality.