Abstract
High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.
Highlights
The MEMS accelerometer is a new kind of sensor that is made by microelectronics and micro-machining technology
The Fourier Transform needs to extract the signal with all the information in the time domain, which is a kind of integral transformation
Wang et al [14,15,16] treated the functional components of each mode obtained by Empirical mode decomposition (EMD) decomposition with threshold value, and reconstructed them to achieve the purpose of noise removal
Summary
The MEMS accelerometer is a new kind of sensor that is made by microelectronics and micro-machining technology. EMD decomposition is combined with wavelet threshold denoising method, and continuous mean square error criterion is introduced to obtain energy demarcation points to determine the high-frequency IMF component that needs noise reduction. The signal after noise reduction is obtained through signal reconstruction This is a new method of noise reduction, which avoids the disadvantage of direct loss of useful information on high frequency components caused by EMD. The continuous mean square error criterion is more accurate, stable and reliable than other boundary methods such as artificially independent boundary, correlation coefficient criterion and energy criterion This joint denoising method is suitable for the removal of high overload MEMS accelerometer signals with shock and vibration characteristics. In order to improve the performance of MEMS accelerometer during High-G calibration, a new combined desensitization method based on EMD and wavelet threshold is proposed. The structure of this paper is as follows: part 2 describes the proposed algorithm, part 3 introduces the accelerometer, part 4 gives the experiment and verification, and the last part is the conclusion
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