The influence of the ambient pressure on the load capacity of near-field acoustic levitation is studied. Theoretical models based on the acoustic radiation pressure and hydrodynamic lubrication theory are built, taking into account the changes of the ambient pressure. Both simulation and measured results show that the levitation force is positively proportional to the ambient pressure. The levitation force increased by 4–5 times at an ambient pressure of 0.5 MPa. A maximum load capacity of 0.309 MPa is obtained.
An empirical mode decomposition (EMD) based time-frequency denoising algorithm is derived in this paper to accomplish the extraction of active ingredients for the multi-frequency mixed signals, and then the self-sensing of vibration signals is realized without additional sensors for the ultrasonic assisted grinding device. The EMD is employed to accomplish the signal decomposition, and multiple intrinsic mode function (IMF) components and the residual are obtained. Then, the weighted factors used for signal reconstruction are obtained based on innovation statistical distance, which is selected as a criterion to evaluate the time-frequency domain correlation between decomposition results and the original signal. Next, the maximum correntropy criterion (MCC) based similarity detection method is designed to adaptively modify the weighted factors, and the signal is reconstructed on the foundation of the modified weighted factors. Finally, numerical simulations and self-sensing experiment are conducted to verify the denoising performance of the proposed algorithm. The self-sensing of 4.59 dB vibration signal is realized for the ultrasonic assisted grinding device.
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