High-g MEMS Accelerometer Research Articles

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

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.

A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF

High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [kbest, abest] could be obtained, in which the permutation entropy (PE) and fuzzy entropy (FE) were selected for MOPSO as fitness functions. Secondly, the accelerometer voltage output signals were decomposed by the improved VMD, then some intrinsic mode functions (IMFs) were achieved. Thirdly, sample entropy (SE) was introduced to classify those IMFs into information-dominated IMFs or noise-dominated IMFs. Then, the short-window TFPF was selected for denoising information-dominated IMFs, while the long-window TFPF was selected for denoising noise-dominated IMFs, which can make denoising more targeted. After reconstruction, we obtained the accelerometer denoising signal. The denoising results of different denoising algorithms in the time and frequency domains were compared, and SNR and RMSE were taken as denoising indicators. The improved VMD and TFPF denoising method has a smaller signal distortion and stronger denoising ability, so it can be adopted to denoise the output signal of the High-G MEMS accelerometer to improve its accuracy.

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

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.

A New Joint Denoising Algorithm for High‐G Calibration of MEMS Accelerometer Based on VMD‐PE‐Wavelet Threshold

Recently, the High‐G MEMS accelerometer (HGMA) has been used in navigation, mechanical property detection, consumer electronics, and other fields widely. As the core component of a measuring system, it is very crucial to enhance the calibration accuracy of the accelerometer. In order to remove the noises in the accelerometer output signals to enhance its calibration accuracy, a combined denoising method which combines variational mode decomposition (VMD) with permutation entropy (PE) and wavelet threshold is given in this article. For the sake of overcoming the defect of signal distortion caused by the traditional denoising methods, this joint denoising method combines the good decomposition characteristics of VMD and the good denoising ability of wavelet threshold and introduces PE as a judgment criterion to achieve a good balance between denoising effect and signal fidelity. The combination of PE and VMD not only avoids the phenomenon of mode aliasing but also improves the ability to identify the noise components, which makes the wavelet threshold denoising more specific. Firstly, some intrinsic mode functions (IMFs) are obtained by using VMD to decompose the complex signal containing noise which is outputted from the accelerometer. Secondly, the IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE algorithm. Thirdly, the noise IMF components can be discarded directly, and then the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained. Finally, the final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and the useful IMF components retained before denoising. The experimental results prove that the combined denoising algorithm combines the merits of VMD, PE, and wavelet threshold, and this new algorithm has a good performance in the calibration denoising of accelerometer. Compared with the serious signal distortion caused by using only EMD or wavelet threshold, this method not only has a good denoising effect (the noises in the static part are eliminated by 99.97% and the SNR of the dynamic part is raised to 18.56) but also can maintain a good signal fidelity (the error of shock peak amplitude is 3.4%, the error of vibration peak amplitude is 0.4%, and the correlation coefficient between the denoising signals and dynamic part is as high as 0.982).

Investigation and experiment of high shock packaging technology for High-G MEMS accelerometer

High-G MEMS accelerometer (HGMA) has applications in aerospace, explosive and penetrative environments, and so on. Reliable encapsulation is the basis of ensuring the survival and function of the sensor in harsh environments, but complete high-g accelerometer sensor encapsulation models are rarely reported. We use a multiple-degree-of-freedom system and the theory of stress wave interface transfer to design and analyze three different packaging methods for high-g accelerometer sensors. We also determine the best encapsulation method by establishing natural frequency calculation and stress wave transfer models between media. The stress wave reflection ability of patch adhesive is verified with a split Hopkinson pressure bar (SHPB). The results indicate that the unloading efficiency of patch adhesive to the stress wave is up to 95%. The HGMA frequency response is greater than 300 kHz, which was measured by a single Hopkinson rod. The anti-impact performance and the measurement accuracy in the ultra-high overload environment of the high-g acceleration sensor were verified by artillery penetration tests. The results show that the sensor can realize high precision measurements under ultra-high overload environments (the measuring accuracy of the sensor under the impact of 200,000 g is better than 5%).

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

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.

High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold.

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%.

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

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.

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

This paper presents a high-G MEMS accelerometer (HGMA). HGMA employs our-beams and central-island mass silicon structure, which features a robust stability in shock environment. Theoretical analysis is conducted to investigate the influence of structure parameters on the Von Mises stress distribution, mechanical sensitivity and natural frequency. With consideration of smaller stress, higher mechanical sensitivity and natural frequency, the structure parameters are optimized and the theoretical sensitivity of HGMA is calculated as 0.488μV/g. Then, the optimized structure is analyzed with finite element analysis software, which shows a maximum stress of 23.19 MPa and a frequency response of 408.19 kHz when a 100 000 g shock is loaded. Finally, a processing flow is designed and the structure is fabricated. Hopkinson bar is utilized to calibrate HGMA sample, and shows an experimental sensitivity of 0.5611μV/g. Long-term static bias and temperature experiments are arranged to evaluate HGMA. Test results verify the presented theoretical analysis, processing flow, and experiment method, which are of great value for guiding the design, fabrication and calibration of other HGMAs.

Near-Contact Gas Damping and Dynamic Response of High-g MEMS Accelerometer Beams

This paper introduces and experimentally validates a new model for near-contact gas damping of microbeams. The model is formulated based on numerical simulations of rarefied gas dynamics using the Boltzmann Ellipsoidal Statistical Bhatnagar-Gross-Krook (ES-BGK) equation. The result is compared with existing models by simulating the motion of beams under high-g acceleration. To experimentally validate the damping models, single crystal silicon MEMS g-switches with cantilever microbeams of various lengths were utilized. The experimental measurements of beam dynamics under peak accelerations of approximately 50,000 g and acceleration ramp rates from 600 to 3,000 g/μs are compared with simulations. Additionally, the damping coefficients are extracted from existing vibrational mode data, and the resulting values are compared to the various models. The new near-contact model was found to predict contact and release times within a root-mean-square deviation from experiment below 9 and 7 for contact and release events, respectively. The damping values for the vibrational modes away from contact were predicted within 33% error, showing a more consistent predictive capability than provided by earlier models.

Design and Test of High-g MEMS Accelerometer

For measuring the high-g impaction and overload of some conditions, a kind of high-g accelerometer was designed. Testing and analysis to the accelerometer was processed through launching projectile to penetrate concrete target. Four fixed end structure was adopted in the design of the high-g accelerometer and the chamfer was set in the bottom and top part of the beam to divert stress. It is learned that the sensitivity of the accelerometer is 0.1uv/g by calibration. A kind of deceleration measurement device was provided to test the performance of the accelerometer through penetration experiment over concrete target and the design of the measurement device and penetration experiment over concrete target was illustrated. The impaction characteristic of the accelerometer was tested and studied through penetration to the target. MATLAB software is used to analyze the influence of the filter cutoff frequency over the test result and the best cutoff frequency point was set at 6 kHz, under which the maximum penetration acceleration is up to 28526 g.