A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

Ma, Tiancheng; Li, Zongcheng; Cao, Huiliang; Shen, Chong; Wang, Zhijian

doi:10.1109/access.2019.2951612

Cited by 19 publications

(14 citation statements)

References 31 publications

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“…The SA algorithm is inspired by the physical process of heating a metal beyond the melting point and then gradually lowering the temperature to end up with a solid with minimum structural defects 29,30 . SA can be adopted for the design optimization of MEMS devices [31][32][33] . A similar approach was used for the design of an AMPACC.…”

Section: Resultsmentioning

confidence: 99%

Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

et al. 2020

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This paper describes a novel, semiautomated design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can design MEMS devices comprising freeform geometries and optimize such MEMS devices to provide high sensitivity, large bandwidth, and large fabrication tolerances. The proposed method does not require much computation time or memory. The use of freeform geometries allows more degrees of freedom in the design process, improving the diversity and performance of MEMS devices. A MEMS accelerometer comprising a mechanical motion amplifier is presented to demonstrate the effectiveness of the design approach. Experimental results show an improvement in the product of sensitivity and bandwidth by 100% and a sensitivity improvement by 141% compared to the case of a device designed with conventional orthogonal shapes. Furthermore, excellent immunities to fabrication tolerance and parameter mismatch are achieved.

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Section: Resultsmentioning

confidence: 99%

Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

et al. 2020

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“…2 )) (16) In this paper, a wavelet transform and FLP are combined, to improve the accuracy of the filter effectively. In general, the steps of the WFLP algorithm are implemented as follows:…”

Section: Wavelet Transform and Forward Linear Prediction Algorithm (Wmentioning

confidence: 99%

“…Serial processing methods consist of first denoising the signal with a filter and then establishing the temperature error model to compensate for the drift, the efficiency of which is low. In this paper, parallel processing [15,16] is employed, which implies that the noise and drift in the gyroscope signal are handled synchronously. Empirical mode decomposition (EMD) [17,18] and wavelet decomposition [19] are a popular multi-scale analysis method.…”

Section: Introductionmentioning

confidence: 99%

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

2020

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For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

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“…Many attempts have been made to improve the above deficiencies by studying temperature characteristics of MEMS gyroscopes. Typically, Ma [2] proposed a parallel denoising model based on PE-ITD (Permutation entropy-Intrinsic time-scale decomposition) and SA-ELM(Simulated annealing-Extreme learning machine) in order to solve the temperature drift of MEMS gyroscopes. Result shows that the angular random walk is decreased obviously from 0.0104 • /h/p Hz to 2.665×10 −5• /h/p Hz, and the bias stability improves from0.1874 • /h to 1.599×10 −3• /h with a temperature range This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

Cao

Liu

Liu³

et al. 2021

IEEE Access

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To eliminate the humidity drift of gyroscope, a new fusion algorithm (IAWTD-CSVM-EEMD) based on Improved Adaptive Wavelet Threshold De-noising (IAWTD), C-means Support Vector Machine (CSVM) and Ensemble Empirical Mode Decomposition (EEMD) is proposed in this paper. In the new fusion algorithm, the humidity drift component is obtained by denoising the output signal of MEMS gyroscope by IAWTD. Then, the humidity drift compensation model is established, the relative humidity, the change rate of relative humidity and the humidity drift component are taken as the input of the compensation model, and the final gyroscope output can be obtained by using EEMD to denoise the output of compensation model. To verify the compensation effect of the fusion algorithm, the gyroscope outputs are collected and analyzed with the relative humidity ranged from 40% to 90% based on the temperature varying from 20℃ to 60℃. The results show that the IAWTD-CSVM-EEMD method significantly reduces the influence of relative humidity drift on the gyroscope output, according to the quantitative analysis of Allan variance, the quantization noise of the gyroscope output decreases by 87.78%, 96.37%, 97.77%, 99.17% and 92.62% respectively under the relative humidity ranging from 40% to 90%, as the temperature rose from 20 ℃ to 60 ℃ at intervals of 10 ℃, In addition, the bias stability decreases by 96.9%, 99.41%, 99.1%, 99.46%, and 99.78% respectively and the angle random walk decreases by 88.16%, 96.54%, 98.16%, 94.43%, and 92.05% respectively at different temperatures. It is worth mentioning that, to further verify the applicability of the fusion algorithm, a group of comparative experiments are added to consider the influence of temperature changes on the gyroscope output under different relative humidity. The experimental results show that the quantization noise, bias stability and angle random walk of the MEMS gyroscope are significantly reduced compared with the original output after processing by IAWTD-CSVM-EEMD. Therefore, the method proposed in this paper is beneficial to reduce the humidity drift in the MEMS gyroscope output.

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A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

Cited by 19 publications

References 31 publications

Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

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