Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Lu, Qijing; Shen, Chong; Cao, Huiliang; Shi, Yunbo; Liu, Jun

doi:10.1155/2019/3154845

Cited by 10 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this in mind, it is necessary to perform noise reduction on the original signal before ELM training. The wavelet transform, with its good time-frequency localization and multi-resolution characteristics, can overcome a series of challenges in non-stationary signal processing, and wavelet threshold noise reduction can effectively remove noise while maintaining signal singularity [ 37 , 38 , 39 ].…”

Section: Random Error Modelling Based On Igwpso-elmmentioning

confidence: 99%

A Random Error Suppression Method Based on IGWPSO-ELM for Micromachined Silicon Resonant Accelerometers

et al. 2023

View full text Add to dashboard Cite

There are various errors in practical applications of micromachined silicon resonant accelerometers (MSRA), among which the composition of random errors is complex and uncertain. In order to improve the output accuracy of MSRA, this paper proposes an MSRA random error suppression method based on an improved grey wolf and particle swarm optimized extreme learning machine (IGWPSO-ELM). A modified wavelet threshold function is firstly used to separate the white noise from the useful signal. The output frequency at the previous sampling point and the sequence value are then added to the current output frequency to form a three-dimensional input. Additional improvements are made on the particle swarm optimized extreme learning machine (PSO-ELM): the grey wolf optimization (GWO) is fused into the algorithm and the three factors (inertia, acceleration and convergence) are non-linearized to improve the convergence efficiency and accuracy of the algorithm. The model trained offline using IGWPSO-ELM is applied to predicting compensation experiments, and the results show that the method is able to reduce velocity random walk from the original 4.3618 μg/√Hz to 2.1807 μg/√Hz, bias instability from the original 2.0248 μg to 1.3815 μg, and acceleration random walk from the original 0.53429 μg·√Hz to 0.43804 μg·√Hz, effectively suppressing the random error in the MSRA output.

show abstract

Section: Random Error Modelling Based On Igwpso-elmmentioning

confidence: 99%

A Random Error Suppression Method Based on IGWPSO-ELM for Micromachined Silicon Resonant Accelerometers

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In order to accurately verify the rationality and usability of the designed sensor, LS-DYNA 19.2 finite element simulation software was used to produce and solve the whole process of projectile intrusion into the concrete target plate and obtain the overload signal of the smart fuze inside the projectile during the projectile intrusion, which supports the accurate layer counting identification of the subsequent fuze [ 26 ].…”

Section: Design Principlementioning

confidence: 99%

Structural Design of MEMS Acceleration Sensor Based on PZT Plate Capacitance Detection

Cui,

Chuai,

Huang

et al. 2023

Micromachines

View full text Add to dashboard Cite

The problem that the fuze overload signal sticks and is not easily identified by the counting layer during the high-speed intrusion of the projectile is an important factor affecting the explosion of the projectile in the specified layer. A three-pole plate dual-capacitance acceleration sensor based on the capacitive sensor principle is constructed in this paper. The modal simulation of the sensor structure is carried out using COMSOL 6.1 simulation software, the structural parameters of the sensor are derived from the mechanical properties of the model, and finally the physical sensor is processed and fabricated using the derived structural parameters. The mechanical impact characteristics of the model under different overloads were investigated using ANSYS/LS-DYNA, and the numerical simulation of the projectile intrusion into the three-layer concrete slab was carried out using LS-DYNA. Under different overload conditions, the sensor was tested using the Machette’s hammer test and the output signal of the sensor was obtained. The output signal was analyzed. Finally, a sensor with self-powered output, high output voltage amplitude, and low spurious interference was obtained. The results show that the ceramic capacitive sensor has a reasonable structure, can reliably receive vibration signals, and has certain engineering applications in the intrusion meter layer.

show abstract

“…After the threshold processing, the reconstruction coefficient

is obtained, and the denoising signal

is obtained from the reconstruction coefficient. The noise standard value σ is expressed as follows, of which median

is the median of wavelet multi-resolution decomposition coefficient [ 23 , 24 ].

…”

Section: Algorithmmentioning

confidence: 99%

Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Shi

Zhang

Jiao

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

show abstract

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Cited by 10 publications

References 27 publications

A Random Error Suppression Method Based on IGWPSO-ELM for Micromachined Silicon Resonant Accelerometers

A Random Error Suppression Method Based on IGWPSO-ELM for Micromachined Silicon Resonant Accelerometers

Structural Design of MEMS Acceleration Sensor Based on PZT Plate Capacitance Detection

Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Contact Info

Product

Resources

About