MEMS Accelerometer Calibration Denoising Method for Hopkinson Bar System Based on LMD-SE-TFPF

Abstract

High-G MEMS accelerometer (HGMA) is widely used in the aerospace field and the precise control of missiles. Therefore, its calibration accuracy is critical to sensor performance and the overall control system. In order to decrease the influence of noise on the HGMA output signal, a hybrid denoising algorithm which is based on the Time-frequency peak filtering (TFPF), Local mean decomposition (LMD) and Sample entropy (SE) has been proposed in this article. For the problem that the TFPF algorithm is limited in the choice of window length, LMD and SE are used to distinguish components, which can improve the TFPF algorithm effectively. It provides a better balance between noise reduction and signal fidelity. Firstly, the noise-containing signal can be decomposed by LMD to obtain PFs. Secondly, calculate the sample entropy values of each PFs, then divide the signal into mixed component, useful component and noise component according to the similarity of sample entropy. Thirdly, the mixed component can use long-window TFPF to reduce noise, the short-window TFPF can reduce the noise for the useful component, and the noise component can be wiped off directly. Finally, the useful component and the mixed component are both reconstructed to form the final denoised signal. Experiments have showed that this method can not only remove noise (the noise of static signal is reduced by 91.76%, the signal-noise ratio of dynamic signal has increased to 17.6), but also retain the details of frequency and amplitude (the shock peak amplitude error is 0.062% and the vibration amplitude error is 0.04%). Therefore, this method can reduce the noise of the High-G MEMS accelerometer signal with maintaining the characteristics of the original signal, thereby greatly improves the performance of the accelerometer, making it widely used. INDEX TERMS High-G MEMS accelerometer (HGMA), denoising, local mean decomposition (LMD), sample entropy (SE), time-frequency peak filtering (TFPF), Hopkinson bar.

Structural Design of MEMS Acceleration Sensor Based on PZT Plate Capacitance Detection

“…Generally speaking, the input acceleration range of a capacitive sensor structure with a circular plate is greater than that of a capacitive sensor structure with a square plate of the same size [ 31 ]. Therefore, the electrode plate and dielectric layer are designed to be circular [ 33 ]. The common operating principles of capacitive sensors include variable pole pitch, variable area, and a variable dielectric constant [ 34 ].…”

High-G MEMS Accelerometer Calibration Denoising Method Based on EMD and Time-Frequency Peak Filtering

“…Algorithm compensation is more convenient and flexible than hardware-based compensation. Yan proposed a hybrid denoising algorithm based on time-frequency peak filtering (TFPF), local mean decomposition (LMD) and sample entropy (SE) to decrease the influence of noise on the high-g MEMS accelerometer (HGMA) output signal [ 17 ]. Guo presented a Kalman filtering method based on information fusion.…”

Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

“…Dake Chen proposed a new method for temperature-independent sensitivity optimization of MEMS accelerometers using wavelet neural network [ 19 ]. ZeYu Yan proposed a hybrid algorithm that combines Time-frequency peak filtering (TFPF), Local mean decomposition (LMD) and Sample entropy (SE) to reduce noise of high-G MEMS accelerometer signals [ 20 ]. For the temperature compensation of high-g MEMS accelerometer (HGMA), Min Zhu proposed four algorithms: radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), RBF NN based on GA with Kalman filter (KF), and the RBF NN + GA + KF method compensated by the temperature drift model [ 21 ].…”