Design, fabrication and calibration of a high-G MEMS accelerometer

Shi, Yunbo; Zhao, Yu Hong; Feng, Hengzhen; Cao, Huiliang; Tang, Jun; Li, Jie; Zhao, Rui; Liu, Jun

doi:10.1016/j.sna.2018.07.010

Cited by 38 publications

(20 citation statements)

References 12 publications

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“…The original signal collected in this paper is from a newly designed and manufactured high-G MEMS accelerometer (HGMA) [26,27]. It has a high impact survival rate and high measuring range.…”

Section: Work Mode Analysismentioning

confidence: 99%

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Zhu

Pang²,

Zhang

et al. 2019

Applied Sciences

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In this paper, the method for compensating the temperature drift of high-G MEMS accelerometer (HGMA) is proposed, including 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. First, this paper introduces an HGMA structure working principle, conducts a finite element analysis, and produces the results. The simulation results show that the HGMA working mode is the 1st order mode, and its resonant frequency is 408 kHz. The 2nd order mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, indicating that the coupling movement between the two modes is tiny, so the HGMA has good linearity. Then, a temperature experiment is performed to obtain the output value of HGMA. The output values of HGMA are analyzed and optimized by using the algorithms proposed in this paper. The processing results show that the RBF NN + GA + KF method compensated by the temperature drift model achieves the best denoing consequent. The processing results show that the temperature drift of the HGMA is effectively compensated. The final results show that acceleration random walking improved from 17130 g/h/Hz0.5 to 765.3 g/h/Hz0.5, and bias stability improved from 4720 g/h to 57.27 g/h, respectively. The results show that after using the RBF NN + GA + KF method, combined with the temperature drift model, the temperature drift trend and noise characteristics of HGMA are well optimized.

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Section: Work Mode Analysismentioning

confidence: 99%

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Zhu

Pang²,

Zhang

et al. 2019

Applied Sciences

Self Cite

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“…e beam and mass are rectangular and easy to manufacture. e beam and mass structure are supported by a frame and connected to the bottom [24,25]. Its structure diagram and size are shown in Figure 4.…”

Section: Structure and Structural Parameters Of The High-g Mems Accelmentioning

confidence: 99%

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Shen

Cao

et al. 2019

Shock and Vibration

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In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer.

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“…Temperature is 25 • C (room temperature), and the sampling rate is 20MHz. Figure 6 shows a schematic and experimental setup of a dynamic linear incremental impact system using a developed dual-warhead Hopkinson bar [8]. The impact measurement system includes a launch and impact system, a measured accelerometer system, a pressure control system and a data acquisition system, as shown in Figures 6(a), (b).…”

Section: B Experimentsmentioning

confidence: 99%

“…However, due to the inevitable causes of hardware circuits and sensors, the HGMA output signal contains a lot of noise, which could seriously affect the accuracy of the calibration. Therefore, reducing the noise of the HGMA output signal can improve the its performance [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Hou

Zhang³

et al. 2019

IEEE Access

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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.

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Design, fabrication and calibration of a high-G MEMS accelerometer

Cited by 38 publications

References 12 publications

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

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

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