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

Zeyu Yan; Boyang Hou; Jingchun Zhang; Chong Shen; Yunbo Shi; Jun Tang; Huiliang Cao; Jun Liu

doi:10.1109/ACCESS.2019.2935129

IEEE Access (Jan 2019)

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

Zeyu Yan,
Boyang Hou,
Jingchun Zhang,
Chong Shen,
Yunbo Shi,
Jun Tang,
Huiliang Cao,
Jun Liu

Affiliations

Zeyu Yan: Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China
Boyang Hou: Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
Jingchun Zhang: Beijing Huijia Private School, Beijing, China
Chong Shen: ORCiD; Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China
Yunbo Shi: Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China
Jun Tang: Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China
Huiliang Cao: ORCiD; Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China
Jun Liu: Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, China

DOI: https://doi.org/10.1109/ACCESS.2019.2935129
Journal volume & issue: Vol. 7
pp. 113901 – 113915

Abstract

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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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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