Monitoring and identification of microscopic damage during fir loading based on empirical modal decomposition and wavelet packet energy entropy
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摘要:目的
细观损伤是承载木材断裂的主要原因之一。木材的多孔层状结构使其损伤过程变得复杂，针对单一信号处理方法较难充分挖掘木材断裂声发射信号中的细观损伤信息，造成识别信息不充分、不完备的问题。本研究提出通过经验模态分解（EMD）和小波包能量熵结合的信号处理方法，通过声发射无损检测手段，识别杉木加载过程中的细观损伤类型。
方法以杉木为研究对象，进行单轴压缩、双悬臂梁和顺纹拉伸3种单一损伤试验，并对其进行加载过程中声发射信号的采集、监测与分析。通过小波包阈值法消除损伤试验中采集的声发射信号噪声，经由EMD和相关系数计算，分离出最能体现杉木细观损伤特征的本征模态（IMF）分量，并对IMF分量进行基于傅里叶变换的峰值频率分析和小波包能量熵分析，提取杉木细观损伤的特征。
结果（1）EMD和小波包能量熵结合的信号处理方法能够判断杉木加载过程中声发射信号对应的细观损伤类型与构成。（2）杉木不同细观损伤类型的声发射信号对应不同的小波包能量熵区间：胞壁屈曲与塌溃（0.69 ~ 0.99）、层间开裂（1.57 ~ 1.78）、纤维束断裂（1.92 ~ 2.27）。（3）宏观断口观察和电镜显微分析验证了该方法的准确性。
结论经验模态分解–小波包能量熵法避免了声发射信号模态堆叠的影响，并解决了木材细观损伤复杂且难以识别的问题，为杉木木材断裂的早期诊断方法提供了理论支撑。
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关键词:
- 木材细观损伤识别 /
- 声发射 /
- 小波包变换 /
- 能量熵 /
- 经验模态分解(EMD)
Abstract:ObjectiveMicroscopic damage is a primary contributor to wood fracture. The intricate porous laminar structure of wood makes the damage process complex, posing challenges in fully comprehending the microscopic damage information within the acoustic emission signal of wood fracture through a single signal processing method. This limitation results in inadequate and incomplete identification information. This study introduced a signal processing approach that combined empirical modal decomposition (EMD) and wavelet packet energy entropy to discern the various types of microscopic damage occurring during the loading process of fir (Cunninghamia lanceolata) using acoustic emission nondestructive testing.
MethodThree individual damage tests, namely uniaxial compression, double cantilever beam, and parallel tensile were conducted on fir as the study object. Acoustic emission signals were acquired, monitored, and analyzed throughout the loading processes. The wavelet packet thresholding method was employed to eliminate noise from the acoustic emission signals recorded during the damage tests. Furthermore, the EMD method, coupled with correlation coefficient calculations, was utilized to isolate the intrinsic mode function (IMF) components, which can fully reflect the characteristics of microscopic damage in fir. Subsequently, Fourier-transform-based peak frequency analysis and wavelet-packet energy entropy analysis were executed on the IMF components to extract the features associated with the microscopic damage in fir.
Result(1) The combination of EMD and wavelet packet energy entropy effectively determined the type and composition of signals corresponding to microscopic damage. (2) Acoustic emission signals of different microscopic damage types corresponded to distinct wavelet energy entropy intervals: buckling and collapse of cell wall (0.69−0.99), delamination (1.57−1.78), and fiber bundle breakage (1.92−2.27). (3) The accuracy of the method was verified by macroscopic fracture and scanning electron microscopy experiments.
ConclusionThe combination of EMD and wavelet packet energy entropy can avoid the influence of modal stacking in acoustic emission signals, and resolve the hard problem of recognizing complex microscopic damages in wood. This approach offers theoretical basis for the early diagnosis of fir wood fractures.
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图 3 单一损伤试验中采集的AE信号及其FFT频域图
Figure 3. AE signals and their FFT frequency-domain plots acquired in single damage tests
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图 4 单轴压缩试验AE信号及IMF分量频域图
Figure 4. AE signal and IMF component frequency domain of uniaxial compression test
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图 5 双悬臂梁试验AE信号及IMF分量频域图
Figure 5. AE signal and IMF component frequency domain of double cantilever beam test
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图 6 顺纹拉伸试验AE信号及IMF分量频域图
Figure 6. Parallel tensile test AE signal and IMF component frequency domain
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图 8 单一损伤试验对应损伤的SEM图
Figure 8. SEM maps of damage corresponding to a single damage test
表 1 单一损伤试验IMF分量和原信号之间的相关系数
Table 1 Correlation coefficients between the IMF components of the single damage test and the original signal
试验类型
Test typeIMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 残余分量
Residual component单轴压缩 Uniaxial compression 0.2270 0.8885 0.1338 0.0170 0.0080 0.0039 0.0028 0.0045 双悬臂梁 Double cantilever beam 0.9459 0.3756 0.0348 −0.0015 0.0005 0.0002 0.0002 −0.0011 顺纹拉伸 Parallel tensile 0.8394 0.4968 0.2006 0.0923 −0.0018 −0.0030 0.0008 −0.0032 
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表 2 单一损伤试验IMF分量和原信号之间的相关系数及其小波包能量熵
Table 2 Correlation coefficients between the IMF components of the single damage test and the original signal,and their wavelet packet energy entropy
AE信号
AE signalIMF1相关系数
Correlation coefficient of IMF1IMF1能量熵
Energy entropy of IMF1IMF2相关系数
Correlation coefficient of IMF2IMF2能量熵
Energy entropy of IMF2UC1 0.3632 1.8692 0.9232 0.6998 UC2 0.2270 2.6569 0.8885 0.7324 UC3 0.5673 1.9258 0.8617 0.7962 UC4 0.3382 2.1592 0.8537 0.8357 UC5 0.4808 2.2873 0.7332 0.9843 DCB1 0.9459 1.5769 0.3756 0.9873 DCB2 0.9121 1.7701 0.3948 1.0595 DCB3 0.9258 1.6938 0.3551 1.0326 DCB4 0.9299 1.7498 0.3299 1.0342 DCB5 0.9245 1.7052 0.3376 1.0047 PT1 0.8394 2.0463 0.4968 1.6399 PT2 0.9013 1.9292 0.3192 1.4839 PT3 0.8805 2.1697 0.4161 1.4556 PT4 0.8638 2.2034 0.4247 1.5903 PT5 0.8595 2.2647 0.4186 1.4179 注：UC1 ~ UC5分别代表单轴压缩试验1 ~ 5；DCB1 ~ DCB5分别代表5组双悬臂梁试验1 ~ 5；PT1 ~ PT5分别代表5组顺纹拉伸试验1 ~ 5。Notes: UC1−UC5 represent the uniaxial compression test 1−5. DCB1−DCB5 represent the double cantilever beam test 1−5. PT1−PT5 represent the parallel tensile test 1−5.
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表 3 不同的细观损伤类型对应的峰值频率
Table 3 Peak frequencies corresponding to different types of microscopic damage
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