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15 pages, 14093 KiB

Open AccessArticle

Integrating Multiple Hierarchical Parameters to Achieve the Self-Compensation of Scale Factor in a Micro-Electromechanical System Gyroscope

by Rui Zhou, Rang Cui, Daren An, Chong Shen, Yu Bai and Huiliang Cao

Micromachines 2024, 15(11), 1385; https://doi.org/10.3390/mi15111385 - 16 Nov 2024

Viewed by 673

Abstract

The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation [...] Read more.

The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm. In this paper, a scale factor prediction model is presented. The model indicates that the resonant frequency and demodulation phase angle are the primary correlation terms of the scale factor, while the drive control voltage and quadrature feedback voltage are the secondary correlation terms of the scale factor. By employing a weighted fusion of correlated terms through PLSR, the scale factor for temperature sensitivity is markedly enhanced by leveraging the predicted results to compensate for the output. The results indicate that the maximum error of the predicted scale factor is 0.124% within the temperature range of −40 °C to 60 °C, and the temperature sensitivity of the scale factor decreases from 6180 ppm/°C to 9.39 ppm/°C. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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15 pages, 4488 KiB

Open AccessArticle

Multi-Frame Vibration MEMS Gyroscope Temperature Compensation Based on Combined GWO-VMD-TCN-LSTM Algorithm

by Ao Li, Ke Cui, Daren An, Xiaoyi Wang and Huiliang Cao

Micromachines 2024, 15(11), 1379; https://doi.org/10.3390/mi15111379 - 15 Nov 2024

Cited by 1 | Viewed by 767

Abstract

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature [...] Read more.

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope’s performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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22 pages, 1364 KiB

Open AccessArticle

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones

by Peng Wang, Jie Dong, Lifu Wang and Shuhui Qiao

Micromachines 2024, 15(10), 1183; https://doi.org/10.3390/mi15101183 - 24 Sep 2024

Viewed by 3466

Abstract

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones [...] Read more.

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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14 pages, 4775 KiB

Open AccessArticle

A Micromachined Silicon-on-Glass Accelerometer with an Optimized Comb Finger Gap Arrangement

by Jiacheng Li, Rui Feng, Xiaoyi Wang, Huiliang Cao, Keru Gong and Huikai Xie

Micromachines 2024, 15(9), 1173; https://doi.org/10.3390/mi15091173 - 22 Sep 2024

Viewed by 1019

Abstract

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized [...] Read more.

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized for the same comb finger area. With the fingers’ length, width, and depth at 120 μm, 4 μm, and 45 μm, respectively, the optimized finger gap ratio is 2.5. The area of the proof mass is 750 μm × 560 μm, which leads to a theoretical thermomechanical noise of 9 μg/√Hz. The accelerometer has been fabricated using a modified silicon-on-glass (SOG) process, in which a groove is pre-etched into the glass to hold the metal electrode. This SOG process greatly improves the silicon-to-glass bonding yield. The measurement results show that the resonant frequency of the accelerometer is about 2.05 kHz, the noise floor is 28 μg/√Hz, and the nonlinearity is less than 0.5%. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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15 pages, 10166 KiB

Open AccessArticle

Dual-Model Derailment Detection Algorithm Based on Variational Bayesian Kalman Filtering

by Shiwei Fan, Xu Gao, Ya Zhang, Huhe Chen, Guoxing Yi and Qiang Hao

Micromachines 2024, 15(8), 939; https://doi.org/10.3390/mi15080939 - 23 Jul 2024

Viewed by 2367

Abstract

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude [...] Read more.

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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16 pages, 9132 KiB

Open AccessArticle

Temperature Compensation for MEMS Accelerometer Based on a Fusion Algorithm

by Yangyanhao Guo, Zihan Zhang, Longkang Chang, Jingfeng Yu, Yanchao Ren, Kai Chen, Huiliang Cao and Huikai Xie

Micromachines 2024, 15(7), 835; https://doi.org/10.3390/mi15070835 - 27 Jun 2024

Viewed by 3831

Abstract

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, [...] Read more.

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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14 pages, 3937 KiB

Open AccessArticle

Optimized Design of Lithium Niobate Tuning Forks for the Measurement of Fluid Characteristic Parameters

by Man Tang, Dehua Chen, Mi Zhang, Feng Jiang and Yu Wang

Micromachines 2023, 14(12), 2138; https://doi.org/10.3390/mi14122138 - 22 Nov 2023

Viewed by 1165

Abstract

The unique double-cantilever beam structure and vibration mode of the tuning fork enable the measuring of fluid density and viscosity synchronously in a decoupling manner. Therefore, it is widely employed in oil and gas development and in petrochemical, food, textile, and other industries. [...] Read more.

The unique double-cantilever beam structure and vibration mode of the tuning fork enable the measuring of fluid density and viscosity synchronously in a decoupling manner. Therefore, it is widely employed in oil and gas development and in petrochemical, food, textile, and other industries. In this paper, quality factors are used to characterize the energy losses of lithium niobate tuning forks when vibrating in a fluid, and the influence parameters, such as length, width, and thickness of the tuning fork arm, etc., of different quality factors are examined with a focus on the viscous quality factor of the fluid. The optimized design of lithium niobate tuning fork dimensions is carried out on this premise, and the analytical solution of the optimal dimension of the lithium niobate tuning fork in the air is obtained. Secondly, the optimal dimension of the lithium niobate tuning fork in fluids is given out by finite element simulation, and the sensitivity of the optimized fork to the viscosity of fluids is investigated. The results show that the optimized tuning fork has a higher quality factor, and thus has a larger parameter measurement range as well as being more sensitive to the change in the fluid density and viscosity. Therefore, the results are of great significance for guiding the preparation and practical application of lithium niobate tuning forks. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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12 pages, 4246 KiB

Open AccessCommunication

A Porous Nanostructured ZnO Layer for Ultraviolet Sensing with Quartz Crystal Microbalance Technique

by Abil S. Asvarov, Arsen E. Muslimov, Soslan S. Makhmudov and Vladimir M. Kanevsky

Micromachines 2023, 14(8), 1584; https://doi.org/10.3390/mi14081584 - 11 Aug 2023

Viewed by 1223

Abstract

Porous films of metals and metal oxides have gained growing attention as potential materials for use in applications that require large, specific surface areas, such as sensors, supercapacitors, and batteries. In this study, a “black-metal”-like porous Zn–ZnO composite layer was grown by room [...] Read more.

Porous films of metals and metal oxides have gained growing attention as potential materials for use in applications that require large, specific surface areas, such as sensors, supercapacitors, and batteries. In this study, a “black-metal”-like porous Zn–ZnO composite layer was grown by room temperature co-sputtering of Zn metal and ZnO:Ga (3 at/%) ceramic targets. Following deposition, a porous ZnO layer was obtained by a subsequent thermal annealing process at 400 °C in air. The morphology and structural properties of the obtained porous layered objects were analyzed. The porosity and chemical characteristics of the nanostructured ZnO layer obtained with the method herein described make it suitable to be used as a sensitivity-enhancing active layered element in quartz crystal microbalance (QCM)-based ultraviolet (UV) sensors. The prepared resonant ZnO/QCM sensors under UV radiation exhibited maximum shift up to 35 Hz for several “on-off” UV cycles, excellent response, and recovery times of 11 and 12 s, respectively. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)

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