Abstract: [Objective] In this study, Pinus yunnanensis is used as the material to analyze the propagation rule of the acoustic emission(AE)signal in samples under different moisture content conditions, and to discuss the response of moisture content to AE signal waveform, which could provide the basis for the location of AE source of P. yunnanensis and the basic data for nondestructive testing of wood internal defects.[Method] The most common P. yunnanensis in Yunnan Province, is used as raw material, containing four kinds of water-bearing states,those were, absolute dry, air-dried, green timber and water-saturated. According to the NI high speed data acquisition equipment and the LabVIEW software, the wood AE signal acquisition platform is set up. Then, the AE signal is collected on the surface of four kinds of wood samples by the simulation of AE source under lead core fracture. Meanwhile, the time difference method is used to calculate the average velocity of four kinds of water condition, and wavelet analysis is used to decompose and reconstruct the AE signal waveform, then soft threshold quantization method is applied for removing each channel coefficient and the high frequency coefficients quantized, and removing non-primary energy signals in order to extract the weak acoustic emission signal from the noise.[Result] In the experiment, the surface wave signals were mainly received by the sensors. With the increase of moisture content, the AE signal waveform and average sound speed of P. yunnanensis are greatly attenuated on the surface. Under absolute dry state, the time domain waveform of AE signal reaches 5.2 V, and the average sound speed can reach 4 208.77 m·s^-1, while the amplitude of the signal waveform is only 0.6 V, and the average sound speed is decreased to 1 414.07 m·s^-1 in water-saturated state. The amplitude and average rate of signal waveforms are ±4 V, ±2 V and 2 328.73 m·s^-1, 3 331.79 m·s^-1, respectively, in air-dried and green timber states, and the average sound velocity difference between each water bearing state is in the range of 876.98-1 003.06 m·s^-1. Moreover, the AE signals submerged in noise can be extracted by the method of wavelet analysis. Thus, the AE signal of four kinds of samples is obtained, and the range of frequency waveform is between 40 and 150 kHz, while the peak value of waveform appears at about 110 kHz in the air dry state, and the other three are peaked at about 50 kHz.[Conclusion] The increase of moisture content significantly changes the AE signal and propagation characteristics of P. yunnanensis, and its signal waveform and average sound speed are positively proportional to the decrease of moisture content. From the comparison of time-frequency diagram of signal before and after wavelet transform, it can be seen that the wavelet transform has obvious advantage in signal noise reduction processing, not only a lot of noise in the signal was removed, but also the useful signal was not damaged, moreover, the signal integrity was guaranteed. On the other hand, to a greater extent, the analysis error is reduced, which gives experimental data support for the research of P. yunnanensis AE source location and internal non-destructive testing. As an AE signal of acquisition and analysis platform, the result of this study could provide the necessary theoretical evidence for the research of AE characteristics of P. yunnanensis with different moisture contents in process of compression deformation and failure.

Key words: Pinus yunnanensis, moisture contents, average sound velocity, acoustic emission characteristics, wavelet analysis