Abstract
High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.
Highlights
Analysis of High-G MEMSAs the manufacturing process of micromechanical systems (MEMS) continues to evolve, machining accuracy continues to increase
It is calculated by the Shannon entropy criterion that the decomposition of five layers is most suitable in the Machete hammer test environment
The root mean square error (RMSE) of the two denoising methods is less than 0.2, and the RMSE of the wavelet packet threshold denoising method is slightly smaller than the wavelet threshold denoising method, which shows that the wavelet packet threshold denoising method in the high-frequency phase better retains the signal waveform, and can better reflect the high-frequency detail information
Summary
As the manufacturing process of micromechanical systems (MEMS) continues to evolve, machining accuracy continues to increase. For MEMS high-G accelerometer used in narrow pulse width and high impact environments, the wavelet denoising method should remove noise, and not affect the normal signal analysis. For the specific application environment of the MEMS high-G accelerometer, signal denoising is performed by using the wavelet threshold denoising method. A series of parameters, such as wavelet threshold denoising and signal-to-noise ratio of wavelet packet threshold denoising, are compared to analyze the wavelet denoising method which is more suitable for a high-G accelerometer
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