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To study the propagation characteristics of acoustic emission signals in Zelkova schneideriana under different boundary conditions, three types of boundary conditions were generated by applying aluminum plates and sound-absorbing cotton on the surface of Zelkova schneideriana specimens. Firstly, the sudden and continuous acoustic emission (AE) sources were simulated by PLB (pencil–lead break) tests and signal generator on the specimen surface, and the AE signals were collected by 5 sensors equally spaced on the surface of the specimen, and the sampling frequency was set to 500 kHz. Then, the detailed signals of different frequency bands were obtained by wavelet decomposition, and TDOA (the time difference of arrival) and correlation analysis method were used to calculate the time difference of longitudinal wave and surface transverse wave and the corresponding propagation velocity, respectively. Finally, the pulse trains with different energy levels generated by the signal generator were used as AE sources to study the attenuation law of AE signal energy with distance under different boundary conditions. The results show that the boundary changes can lead to a significant increase in the surface transverse wave velocity, and have no significant effect on the longitudinal wave velocity. At the same time, the energy attenuation of surface and longitudinal waves is faster after the aluminum plate and sound-absorbing cotton are affixed, and the distance of longitudinal waves attenuation to 90% is reduced from 186 to 139 mm, and the distance of surface transverse waves propagation is reduced from 312 to 226 mm.
To study the propagation characteristics of acoustic emission signals in Zelkova schneideriana under different boundary conditions, three types of boundary conditions were generated by applying aluminum plates and sound-absorbing cotton on the surface of Zelkova schneideriana specimens. Firstly, the sudden and continuous acoustic emission (AE) sources were simulated by PLB (pencil–lead break) tests and signal generator on the specimen surface, and the AE signals were collected by 5 sensors equally spaced on the surface of the specimen, and the sampling frequency was set to 500 kHz. Then, the detailed signals of different frequency bands were obtained by wavelet decomposition, and TDOA (the time difference of arrival) and correlation analysis method were used to calculate the time difference of longitudinal wave and surface transverse wave and the corresponding propagation velocity, respectively. Finally, the pulse trains with different energy levels generated by the signal generator were used as AE sources to study the attenuation law of AE signal energy with distance under different boundary conditions. The results show that the boundary changes can lead to a significant increase in the surface transverse wave velocity, and have no significant effect on the longitudinal wave velocity. At the same time, the energy attenuation of surface and longitudinal waves is faster after the aluminum plate and sound-absorbing cotton are affixed, and the distance of longitudinal waves attenuation to 90% is reduced from 186 to 139 mm, and the distance of surface transverse waves propagation is reduced from 312 to 226 mm.
To assess the damage and fracture behavior of wood under load, a wood damage assessment method was proposed based on acoustic emission (AE) b-values and seismic magnitude difference entropy. First, AE signals from Pinus sylvestris var. mongolica (softwood) and Zelkova schneideriana (hardwood) specimens were collected separately at a sampling frequency of 500 kHz in a three-point bending test. Then, 52 dB was taken as the threshold of the AE event, and the b-value and seismic magnitude difference entropy were calculated at 4-s intervals. Finally, by comparing with the load‒time curve, the b-value and seismic magnitude difference entropy were used to evaluate the damage fracture degree. The results showed that an increase in the b-value indicates the accumulation of strain energy, and vice versa, corresponding to the concentrated release of strain energy. At the same time, the test process can be divided into three stages—elastic, elastic‒plastic and plastic—based on the level of the seismic magnitude difference entropy.
To reveal the propagation characteristics of acoustic emission(AE) signals within the mechanical body of an industrial robot, this study extracted the frequency features and wave velocities of AE signals when propagating on the interior and exterior surfaces.Subsequently,an AE source identification algorithm based on characteristic frequencies and time difference of arrival(TDOA) was proposed.AE sources were generated using Pencil-Lead Break(PLB),and signals were collected with piezoelectric sensors.Next,a denoising process was applied to the raw signals using a 4-layer wavelet decomposition method,and the empirical mode decomposition(EMD) method decomposed the denoised AE signals into independent modal functions(IMFs).The AE waveforms were then reconstructed using information from correlation coefficients and variance accounted for(VAF),enabling the extraction of frequency domain characteristics when AE signals propagated on the interior and exterior surfaces of the mechanical body.Frequency domain characteristics were extracted, and propagation speeds were calculated using the TDOA principle.A localization algorithm based on geometric relationships was developed.Results show exterior coating significantly affects AE propagation characteristics.Velocities on interior and exterior surfaces are 4463m/s and 4743m/s.The characteristic frequency ranges measured by the two sensors are 130kHz ~ 145kHz and 150kHz ~ 170kHz on the exterior surface, and 110kHz ~ 120kHz and 130kHz ~ 140kHz on the interior surface.The localization accuracy of the proposed AE source localization algorithm ranges from 0.2%~2.9%.
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