The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the “collapse” time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system “crash” time for beech and 4 s in advance of the system “crash” time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

为了通过声发射信号(AE)对木材内部损伤状态进行评估,本研究选择能够表征信号复杂程度(熵值)以及波形变化(脉冲因子、裕度因子、峰值因子、波形因子)的特征,并通过对木材试样三点加压弯曲实验采集原始声发射信号.另外,为了抑制干扰,提高信号特征对损伤状态的敏感性,提出了一种采用EMD分解方法,基于信号能量、瞬时频率、峭度值的信号预处理方法和信号重构机制.最后,获取重构AE信号的熵值(信息熵、指数熵)和波形特征参数(脉冲因子、裕度因子、峰值因子、波形因子),探索了它们与木材内部损伤与断裂过程的关系,并从中提取6段时序信号进行特征值前后对比分析.结果表明:通过这些特征,可以将木材受力激发出的声发射信号分为4个类别,分别是屈曲AE信号、形变AE信号、微裂AE信号和断裂AE信号,并与木材损坏过程中微观结构变化的4种形式(胞壁屈曲与塌溃、胞壁界面损伤与层裂、微裂隙损伤区的形成与扩展、胞壁断裂)相对应.相比较波形特征,声发射信号的熵值能够更加敏感地反映出木材内部损伤状态的变化.波形特征能够较好地反映出木材断裂后载荷逐渐减小的趋势.本研究提出的信号预处理方法和重构机制,能够提高上述特征对不同损伤状态的区分度,同时本研究所选特征在木材AE信号识别中具有重要作用.

The nondestructive testing technology of generated acoustic emission(AE) signals for wood is of great significance for the evaluation of internal damages of wood. In order to improve the classification accuracy and adaptability of AE signal, we selected two features(pseudospectrum, entropy) for classify AE signals in the process of wood fracture using SVM classifier. The three-point bending load damage experiment was utilized to generate original AE signals. Evaluation indexes(Precision, Accuracy, Recall, F1-score, Cohen Kappa score, Matthews Corrcoef) were adopted to assess the classification model. The results showed that the overall accuracy of the SVM classification model obtained by the method combining pseudospectrum and entropy features is 89.44%, which indicates that this automatic classification model has good AE signal recognition performance.

The nondestructive testing technology of generated acoustic emission (AE) signals for wood is of great significance for the evaluation of internal damages of wood. To achieve more accurate and adaptive evaluation, an AE signals classification method combining the empirical mode decomposition (EMD), discrete wavelet transform (DWT), and linear discriminant analysis (LDA) classifier is proposed. Five features (entropy, crest factor, pulse factor, margin factor, waveform factor) are selected for classification because they are more sensitive to the uncertainty, complexity, and non-linearity of AE signals generated during wood fracture. The three-point bending load damage experiment was implemented on sample wood of beech and Pinus sylvestris to generate original AE signals. Evaluation indexes (precision, accuracy, recall, F1-score) were adopted to assess the classification model. The results show that the ensemble classification accuracies of two tree species reach 94.58% and 90.58%, respectively. Moreover, compared with the results of the original AE signal, the accuracy of the AE signal processed by the methods proposed is increased by 27.68%. It indicates that the EMD and DWT signal processing methods and selected features improve the classification accuracy, and this automatic classification model has good AE signal recognition performance.

Nondestructive testing technology of wood acoustic emission(AE) signal is of great significance to evaluate wood internal damage. In order to achieve more accurate and adaptive evaluation, we propose an AE signal analysis method combining instantaneous frequency and power to extract the signal features of different the Intrinsic Mode Function(IMF) components. Then input the SVM classifier for classification and recognition, and adopt the Receiver Operating Characteristic (ROC) curve as the evaluation index to evaluate the classification model of different IMF components. The results show that the instantaneous frequency and power can clearly display AE signal features. The IMF components decomposed by EMD are classified by extracting features, and the classification accuracy of IMF 1 component up to 88% is the highest one. It indicates that IMF 1 component contains a large number of effective AE signal features, which can be utilized for the identification of wood damage and fracture state.