A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD

Ding, Mingkuan; Shi, Zhao; Du, Binhan; Wang, Huaiguang; Han, Lanyi

doi:10.1088/1361-6501/abfe33

Cited by 22 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Zhou et al [28] used particle swarm optimization (PSO) algorithm to optimize the parameters of VMD to extract the fault features in the vibration signal of hydraulic pump. Also, Ding et al [29] used the grid search algorithm to select VMD optimal parameters. Although the performance of VMD can be improved by the parameters optimizations of VMD in the above researches, the performance of VMD may be better when combining the parameters optimization of VMD with multiple decomposition based on VMD.…”

Section: Determination Of the Methodsmentioning

confidence: 99%

An on-line monitoring method for the flow excitation in francis hydraulic turbine based on dynamics

Li¹,

Zhang²,

Liu³

et al. 2022

J. vibroeng.

View full text Add to dashboard Cite

During the operation of hydraulic turbine, the vibration often exceeds the standard due to flow excitation, which affects the safe and stable operation of the hydraulic turbine. In this paper, an on-line monitoring method for the flow excitation in Francis hydraulic turbine is proposed based on the dynamic characteristics of Francis hydraulic turbine. Firstly, based on the flow excitation characteristics in Francis hydraulic turbine and the dynamic equation of main shaft system, the internal relationships between the dynamic response of turbine bearing and the flow excitation in the Francis hydraulic turbine are clarified. Secondly, according to these relationships, a method based on wavelet transform (WT), particle swarm optimization (PSO), and variational mode decomposition (VMD) is proposed for extracting the flow excitation features in Francis hydraulic turbine. Then, by this method, the flow excitation features are acquired. Based on the obtained flow excitation features, the flow excitation can be monitored on-line. Finally, the proposed on-line monitoring method for the flow excitation in Francis hydraulic turbine is verified by experiments. Based on the results, the flow excitation components in the signal extracted by this method are about 35 % more than those extracted by the previous methods. The results show that the on-line monitoring method proposed in this paper is convenient and effective.

show abstract

Section: Determination Of the Methodsmentioning

confidence: 99%

An on-line monitoring method for the flow excitation in francis hydraulic turbine based on dynamics

Li¹,

Zhang²,

Liu³

et al. 2022

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Later, the usual statistical laws gradually gave way to other identification and modeling approaches to describe the noise included in the signals. Examples of these approaches contain: a) power spectral density (PSD) [8]; b) ARMA model technique [9]; c) autocorrelation function [10].…”

Section: Introductionmentioning

confidence: 99%

Development of a hardware emulator of a nanosatellite gyroscope

2023

View full text Add to dashboard Cite

The gyroscope sensor has multiple applications in consumer electronics, aircraft navigation, and control systems. Significant errors that match the corresponding data are a typical disadvantage of this sensor. This needs to be done by making error models that can be used to get the right level of measurement accuracy. For high-precision space applications, the navigation design system should take into account the angle random walk (N), bias instability error (B), and rate random walk (K) of the BMG160 gyroscope. For this reason, this paper shows how to use Allan Variance (AVAR) and Power Spectral Density (PSD) for the experimental identification and modeling of the stochastic parameters of the Bosch BMG160 gyroscope embedded in a nanosatellite in order to get an accurate gyroscope model. This work also demonstrates the principle of operation of the equivalent electronic model intended to carry out advanced simulations without recourse to the real material in order to avoid the problem of bad manipulation and availability of the material in order to reduce the time and cost of development. The interpretation of the Allan curves and the PSD obtained from the measurements collected over a long period is presented, as well as a comparison between the real raw data of the BMG160 gyroscope and the designed hardware emulator in both the time and frequency domains. This is done to evaluate the accuracy of the gyroscope model emulating the real sensor in laboratory simulations. The experimental results show that the signals from the emulator and the BMG160 gyroscope are quite close. Therefore, the proposed prototype could be an optimal solution for laboratory calculations and simulations

show abstract

“…Nowadays, there are two main methods for compensating random errors in MEMS gyroscopes: digital signal processing and modeling compensation [3]. Digital signal processing includes methods such as wavelet denoising [4], empirical mode decomposition (EMD) [5], variational mode decomposition (VMD) [6], and filtering. The main modeling methods include time series modeling [7] and artificial intelligence (AI) modeling [8].…”

Section: Introductionmentioning

confidence: 99%

A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

Zeng,

Xian,

Liu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

The random error in micro-electro-mechanical systems (MEMS) gyroscopes is one of the major aspects that limit measurement accuracy. In order to address the inaccurate extraction of noise and trend during the signal preprocessing, as well as the subjectivity in autoregressive moving average (ARMA) model ordering, this paper proposes a method based on interval empirical mode decomposition and ARMA model. In the proposed method, the original signal is decomposed into a series of intrinsic mode functions (IMFs) and a residual through empirical mode decomposition (EMD). Based on the Hellinger distance and autocorrelation function, IMFs are then classified into noise IMFs, hybrid IMFs, and signal IMFs. The improved sand cat swarm optimization is utilized to optimize the ordering process of the ARMA model. The improved adaptive filter is adopted to compensate the random error, and the compensated signal is reconstructed with the signal IMFs and residual to obtain the final output. Experiments show that under static conditions, the proposed method could reduce the root mean square error (RMSE) by 52.6% and 33.3%, respectively, compared with the traditional EMD and ARMA methods. Under dynamic conditions, the proposed method could reduce the RMSE by 51.1% and 37.1%, respectively, compared with the traditional EMD and ARMA methods. The proposed method could effectively compensate the random error and improve the measurement accuracy of MEMS gyroscopes.

show abstract

A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD

Cited by 22 publications

References 21 publications

An on-line monitoring method for the flow excitation in francis hydraulic turbine based on dynamics

An on-line monitoring method for the flow excitation in francis hydraulic turbine based on dynamics

Development of a hardware emulator of a nanosatellite gyroscope

A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

Contact Info

Product

Resources

About