Objective Microscopic 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.

Method Three 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.

Conclusion The 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.