Pole-Zero Temperature Compensation Circuit Design and Experiment for Dual-Mass MEMS Gyroscope Bandwidth Expansion

Cao, Huiliang; Zhang, Yingjie; Han, Ziqi; Shao, Xingling; Gao, Jinyang; Huang, Kun; Shi, Yunbo; Tang, Jun; Shen, Chong; Liu, Jun

doi:10.1109/tmech.2019.2898098

Cited by 95 publications

(48 citation statements)

References 26 publications

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“…(6) Calculate mutation and vaccination according to formulas (7)- (10). Calculate the minimum permutation entropy in the population after variation and inoculation, and record the corresponding k and α.…”

Section: Vmd Based On the Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

“…So, composite fault extraction of the gearbox is critical [4][5][6]. At present, the methods used in gearbox fault extraction include Wavelet transform (WT), Empirical mode decomposition (EMD), Local mean decomposition (LMD), and Ensemble empirical mode decomposition (EEMD), these methods successfully extract the fault information, but they will show their own weaknesses when extracting composite faults [7][8][9][10]. When using WT to decompose signals, we need to give a basis function and the number of decomposing layers ahead of time, that is, WT is not adaptive [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

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Section: Vmd Based On the Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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“…Micro-Electro-Mechanical System Inertial navigation system (MEMS-INS) demonstrates its unique superiority, which can calculate the next-point location based on the continuously measured self-motion velocity and direction information, rather than external information, so it has a vast application scope. Whereas, the navigation information of INS is generated by the integration of velocity and direction information measured by sensors, so the error will increase over time, thereby resulting in poor positioning accuracy over a long time [1][2][3]. Additionally, inertial sensors suffer from large measurement uncertainty at slow motion [4].…”

Section: Introductionmentioning

confidence: 99%

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

et al. 2019

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Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

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“…Yang proposed that architecture use an on-chip temperature sensor to achieve on-chip temperature compensation, and a micro-heater to realize on-chip temperature control of the sensor [6]. Cao analyzed the noise and temperature compensation method of the MEMS gyroscope and studied the circuit interface structure and bandwidth of the MEMS sensor, effectively suppressing the noise of the MEMS sensor [7][8][9][10]. To a certain extent, the hardware compensation method does not only increase the volume and power consumption of the accelerometer, but also increases costs, so the hardware method is not applicable in applications that require low cost and low power consumption.…”

Section: Introductionmentioning

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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Pole-Zero Temperature Compensation Circuit Design and Experiment for Dual-Mass MEMS Gyroscope Bandwidth Expansion

Cited by 95 publications

References 26 publications

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

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

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