High-g Accelerometer Research Articles

Development of a MEMS Piezoresistive High-g Accelerometer with a Cross-Center Block Structure and Reliable Electrode.

A MEMS piezoresistive sensor for measuring accelerations greater than 100,000 g (about 106 m/s2) is described in this work. To enhance the performance of the sensor, specifically widening its measurement range and natural frequency, a cross-beam construction with a center block was devised, and a Wheatstone bridge was formed by placing four piezoresistors at the ends of the fixed beams to convert acceleration into electricity. The location of the varistor was determined using the finite element approach, which yielded the optimal sensitivity. Additionally, a reliable Pt-Ti-Pt-Au electrode was designed to solve the issue of the electrode failing under high impact and enhancing the stability of the ohmic contact. The accelerometer was fabricated using MEMS technology, and the experiment with a Hopkinson pressure bar and hammering was conducted, and the bias stability was measured. It had a sensitivity of 1.06 μV/g with good linearity. The simulated natural frequency was 633 kHz The test result revealed that the accelerometer can successfully measure an acceleration of 100,000 g.

Integrated package and calibration of high-g MEMS ASIC acceleration sensor

To achieve the intelligentization and miniaturization goals of high-g acceleration sensors, this paper proposes a super high-g accelerometer using ASIC (Application-Specific Integrated Circuit) and MEMS (Micro-Electro-Mechanical Systems) integrated packaging. The sensor structure is a membrane island structure and the ASIC interface is manufactured using 0.35 µm CMOS (Complementary Metal-Oxide-Semiconductor) technology. Two prototypes were calibrated and the sensitivities are 0.206 µV/g and 0.337 µV/g. The package frequency of sensors is 74.5 kHz. The sensor range is 200,000 g, with an overload resistance of up to 250,000 g, the enhancement of overload resistance of sensors with integrated ASIC chips has been achieved. The resolution of the prototype is 371.10 g, which is 71.95 % higher compared to the sensor without integrated ASIC. The signal-to-noise ratio (SNR) is 72.363 dB, which has been improved by 24.73 %, verifying the effective improvement of resolution and SNR of the sensors with the addition of ASIC chips.

Noise Reduction of High-G Accelerometer Signals Based on Frequency-Domain Segmentation and Time-Domain Zeroing

Noise Reduction of High-G Accelerometer Signals Based on Frequency-Domain Segmentation and Time-Domain Zeroing

Noise Reduction for High-G Accelerometer Signals Using Deep Learning With Residual Dense Module

Noise Reduction for High-G Accelerometer Signals Using Deep Learning With Residual Dense Module

Equal-strength beam design of acoustic wave accelerometers

Abstract Surface acoustic wave (SAW) based accelerometers have received significant attention due to their digital output, low cost, mass production and easy implementation of wireless passive function. However, conventionally rectangular cantilever-beam based SAW accelerometers often have non-uniform strains generated along the beams, which cause emergence of parasitic wave modes and measurement errors. In this paper, a simulation platform was developed to analyze and optimize designs of SAW accelerometers and variable-thickness and equal-strength beams were designed to solve the critical issue of non-uniform strain distribution along the beam. Frequency responses of SAW accelerometers under the acceleration were successfully obtained using the simulation platform, with the visualized strain/stress distribution and particle displacement field. The accuracy of this simulation platform was verified using the experimental result reported in literature. A highly sensitive and equal-strength beam SAW accelerometer was achieved with a sensitivity up to 1.40 kHz g−1, a linearity coefficient of ∼1, and a measurement range of 0∼15 g. Furthermore, a high-G accelerometer was designed, with the capability of enduring large shocks up to 11,500 g and a sensitivity of 6.96 Hz g−1.

Versatile and effective design platform for surface acoustic wave accelerometers

Micro-electro-mechanical system (MEMS) accelerometers have great potentials for applications in aerospace, autonomous driving and consumer electronics. However, their working principles are mostly based on capacitive and resistance types, which cannot be easily and effectively used for wireless and passive sensing, while surface acoustic wave (SAW) technology is the key solution for this problem. Due to complex acoustic-electric-mechanical coupling during SAW accelerators’ operations, currently, there needs an accurate, reliable, and efficient design and simulation platform to improve their structure and performance. In this work, we proposed an accurate, reliable, and efficient modeling platform to optimize designs of SAW accelerometers, using a double-ended cantilever beam structure as an example. This model integrated the elastic acoustic effect and the coupled wave equations under both the mechanical and electrical loading using the finite element analysis, and effectively obtained acceleration-frequency responses of the SAW accelerators. We have systematically simulated effects of thickness of piezoelectric film, wavelength, and structural parameters of cantilever beams, and the simulation results are well consistent with the theoretical ones. Finally, using the developed model, we designed a high-G SAW accelerometer (up to 20000 g) with a high sensitivity (−41.8 Hz g−1) and excellent linearity (0.9999), and another one with a high sensitivity (3.02 KHz g−1) and a good linearity (0.9999) over a 100 g acceleration range.

Reference velocity demodulation method for accelerometer shock testing based on enhanced CEEMD and threshold correction

High-G accelerometers are critical for measuring high shock signals and must be calibrated to improve measurement accuracy. A laser Doppler velocimeter (LDV) is required to calibrate a high-G accelerometer to provide a high-precision reference velocity. The LDV signal must be demodulated to obtain the velocity. However, the phase method is susceptible to noise interference, while the conventional periodic distribution method is challenging to demodulate and severely affected by signal oscillations. We propose a novel periodic distribution method based on enhanced complementary ensemble empirical mode decomposition (CEEMD) and threshold correction to demodulate the LDV signal. First, the LDV signal is processed with CEEMD to obtain multiple intrinsic mode functions (IMFs) and the residual. Next, each IMF is partially zeroed to obtain the noise-reduced LDV signal. Then, the over-threshold peak of the noise-reduced LDV signal is calculated. Finally, the demodulated velocity of the LDV signal is obtained by correcting the noise-reduced LDV signal according to the over-threshold peak point and calculating all the zero points. Simulation and experimental results show that the proposed method outperforms the phase method based on enhanced CEEMD and the periodic distribution method based on enhanced CEEMD and can significantly reduce noise interference. The results show that the proposed method can accurately demodulate the LDV signal to obtain a highly accurate reference velocity, improving the reliability of accelerometer shock testing.

Synchronous dynamic calibration of triaxial high-g accelerometers using a modified Hopkinson bar method: Theory, principle and experiment

Because the inter-axis coupling effect and nonlinearity of triaxial high-g accelerometers (3-Axis-Gs) are significant under high-g and high-frequency loads, calibrating the sensitivity characteristics using a triaxial synchronous method is necessary but experimentally difficult. This study focused on the physics-based sensitivity model of 3-Axis-Gs, as well as the principle and experimental method of triaxial synchronous calibration. First, a sensitivity model of 3-Axis-Gs was established based on the physical source of cross-sensitivity. Then, a new triaxial synchronous calibration method was proposed using the modified Hopkinson pressure bar, which can simultaneously apply excitation accelerations with amplitudes greater than 100,000 g and frequencies greater than 50 kHz along the three axes of 3-Axis-Gs. Finally, a 3-Axis-G was calibrated using both triaxial synchronous and uniaxial calibration methods. Compared with uniaxial calibration, triaxial synchronous calibration can cover the entire test range, whose results can reflect the actual cross-sensitivity characteristics of 3-Axis-Gs in a three-dimensional loading environment.

Prediction on the Performance of Polymer-Based Mechanical Low-Pass Filters for High-G Accelerometers

A polymer-based mechanical low-pass filter(m-LPF) for high-g accelerometers makes it possible to remove high-frequency transient noises from acceleration signals, thus ensuring repeatable and reliable measurement on high-g acceleration. We establish a prediction model for performance of m-LPF by combining a fundamental vibration model with the fractional derivative standard linear solid(FD SLS) model describing the storage modulus and loss modulus of polymers. Here, the FD SLS model is modified to consider the effect of m-LPF shape factor (i.e., thickness) on storage modulus and loss modulus. The prediction accuracy is verified by comparing the displacement transmissibility(or cut-off frequency) estimated using our model with that measured from 3 kinds of polymers(polysulfide rubber(PSR), silicone rubber(SR), and polydimethylsiloxane(PDMS)). Our findings will contribute a significant growth of m-LPF for high-g accelerometers.

A Method for Synchronous Shock Calibration of Triaxial High-g Accelerometers Based on Modified Hopkinson Bar Technique

Since the width of the excitation pulse is generally in the order of microseconds to milliseconds, the key technique in the shock calibration of triaxial high-g accelerometer is how to achieve the synchronous loadings with high-g amplitude along the three orthogonal axes. Given this background, this paper developed a synchronous calibration method for the triaxial high-g accelerometers by modifying the conventional Hopkinson bar method. Using this newly developed method, three-dimensional reference acceleration pulses with amplitude range of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> g-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> g and width range of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> μs-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> μs can be generated and imposed on the triaxial high-g accelerometers synchronously. Then, calibration experiments on a typical triaxial high-g accelerometer were carried out by using both the synchronous calibration method and the conventional Hopkinson bar method, respectively. The sensitivity of the triaxial accelerometer was characterized by a matrix, the so-called sensitivity matrix, containing 3 main sensitivity coefficients and 6 transverse sensitivity coefficients. Least squares method (LSM) was used to calculate the sensitivity matrix. Generalization error was employed to evaluate the measurement accuracy of the accelerometer. The results show that the measurement accuracy of the triaxial high-g accelerometer calibrated by the synchronous method is higher than that calibrated by the conventional asynchronous method. Therefore, the developed synchronous method is recommended to ensure and improve the measurement accuracy of the triaxial high-g accelerometers. Finally, the validity and the calibration ability of the newly developed method were numerically investigated and discussed in detail.

Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing

The laser Doppler velocimeter (LDV) is commonly used in high-G accelerometer shock testing to provide high-precision reference velocity measurements. However, noise inevitably interferes with LDV signals, reducing the measurement accuracy. A novel denoising method based on convolutional neural network with long short-term memory (CNN-LSTM) and complementary ensemble empirical mode decomposition (CEEMD) is proposed to improve the measurement accuracy of reference velocity. First, the weights were obtained by training the constructed CNN-LSTM neural network. CEEMD was then used to process the training signals, and the resulting IMF was partially zeroed. Furthermore, the splitting points were evaluated and optimized. Finally, the weights and optimal splitting points were applied to the test signals. Simulation and experimental results show that the proposed method outperforms wavelet thresholding and CNN-LSTM in denoising performance. The results show that the proposed method can improve the accuracy of the demodulated velocity and thus contribute to accelerometer shock testing.

High-G MEMS Accelerometer Calibration Denoising Method Based on EMD and Time-Frequency Peak Filtering.

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.

Frequency Characteristic Measurement of High-G Accelerometers Based on Down-Step Response

High-G accelerometers are a critical component for the accurate measurement of high-impact signals. Each high-G accelerometer must be frequency calibrated to ensure the reliability of its measurements. However, the existing methods for measuring the frequency response of high-G accelerometers use sinusoidal shocks for excitation, which is not only complex but also prone to noise interference. To overcome this problem, we propose a down-step response method (DSRM) to measure the frequency characteristics of high-G accelerometers. First, the aluminum foam was selected as the cushion material for the shock test to generate the down-step shock signal. Then, the response signal of the high-G accelerometer under the down-step shock excitation was converted into a step response signal. Finally, the features in the step response signal were extracted to calculate the frequency characteristics of the accelerometer. Experimental results show that DSRM outperforms the conventional fast Fourier transform (FFT) and can significantly reduce noise interference while reducing the equipment requirements. The results demonstrate the proposed measurement method’s high reliability, stability, and convenience.

Study on stress isolation structure of piezoresistive high-g accelerometer

We put forward a novel thermal stress isolation structure with five anchors, which greatly suppresses the zero offset of piezoresistive high-g accelerometer. In this paper, the parameters of the thermal stress isolation structure are optimized by finite element simulation to ensure that the sensor has lower zero offset and a good natural frequency. A high natural frequency can prevent the sensor from being damaged due to resonance when it is subjected to shock. Finally, the zero offset of the sensor with thermal stress isolation is reduced to 10% of that without thermal stress isolation. Natural frequency of the sensor is 264.18 kHz. Its sensitivity is 1.1μV/G/5V according to shock simulation of 100,000 g.

A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF

High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [kbest, abest] could be obtained, in which the permutation entropy (PE) and fuzzy entropy (FE) were selected for MOPSO as fitness functions. Secondly, the accelerometer voltage output signals were decomposed by the improved VMD, then some intrinsic mode functions (IMFs) were achieved. Thirdly, sample entropy (SE) was introduced to classify those IMFs into information-dominated IMFs or noise-dominated IMFs. Then, the short-window TFPF was selected for denoising information-dominated IMFs, while the long-window TFPF was selected for denoising noise-dominated IMFs, which can make denoising more targeted. After reconstruction, we obtained the accelerometer denoising signal. The denoising results of different denoising algorithms in the time and frequency domains were compared, and SNR and RMSE were taken as denoising indicators. The improved VMD and TFPF denoising method has a smaller signal distortion and stronger denoising ability, so it can be adopted to denoise the output signal of the High-G MEMS accelerometer to improve its accuracy.

Visualization and quantification of soil laboratory impact compaction

As important methods to guide the field soil compaction, the standard and modified Proctor tests for laboratory compaction have remained unchanged for decades, which should be improved to better understand the compaction process and the properties of soils. In this study, an accelerometer was installed on a Marshall impact compactor to capture the dynamic response of three types of soils during compaction. The experimental test results indicated that the acceleration curve for each blow gradually evolved to a stable pattern following the progress of compaction, and the impact and gyratory locking points were linearly related with coefficient of determination R 2 equal to 0.59. The impact compaction curve could be further constructed by filtering the structural resonance, which can be used to quantify the compactability of soil materials. Although each type of soil had a unique set of compaction curves, the slope and value of compaction curve altered accordingly as the moisture content changed for the same soil. In addition, the average acceleration value at the final compaction stage could serve as the target value of soil stiffness. • A high-g accelerometer was utilized to capture the dynamic response of soil impact compaction. • The impact and gyratory locking points were related for soil compaction. • The soil impact compaction curve could be constructed by filtering the structural ringing.

Kinematic Loggers-Development of Rugged Sensors and Recovery Systems for Field Measurements of Stone Rolling Dynamics and Impact Accelerations during Floods.

Discrete particle dynamics is one of the least understood aspects of river bedload transport, but in situ measurement of stone movement during floods poses a significant technical challenge. A promising approach to address this knowledge gap is to use sensors embedded within stones. Sensors must be waterproof and recoverable after being transported downstream and potentially buried by other sediment. To address this challenge rugged sensors (Kinematic Loggers) were developed for deployment inside stones (ranging in size from cobbles to boulders) during floods. The sensors feature a 9-axis inertial measurement unit, 3-axis high-g accelerometer, 128 MB flash memory, and a 433 MHz LoRa radio transmission module for sensor recovery. The sensors are enclosed in rugged waterproof housings for deployment in extreme conditions (i.e., bedload transport during floods). Novel relay units and drone-based recovery systems were also developed for finding the sensors after field deployments. Firmware to control the sensors and relay units was developed, as well as software for configuring the sensors and an android application for communicating with the sensors via the LoRa radio transmission module. This paper covers the technical development of the sensors, mounting them inside stones, and field recovery tests. Although designed for measurement of coarse bedload transport and particle dynamics during floods, the sensors are equally applicable for deployment in other harsh environments, such as to study landslide and rockfall dynamics.

Research on Dynamic Calibration Technology of Shock Accelerometer Based on Model Method

High-g shock accelerometers usually use model-based dynamic calibration to obtain their dynamic characteristics. Starting from the dynamic calibration technology chain, a different model linearization algorithm from ISO16063-43 is used for accelerometer model establishment and parameter identification, and the Monte Carlo method is used to calculate the uncertainty of model parameters. In order to verify the proposed method, simulated signals were constructed under different shock excitation amplitudes, and the parameter identification and uncertainty evaluation were done on the simulated signals to verify the proposed algorithm. At the same time, the algorithm is tested by using the actual shock accelerometer calibration signal. The results show that the algorithm has achieved good results without increasing the amount of calculation, and has practical significance for the dynamic calibration of accelerometers and other sensors in practical engineering applications.

Self-validating high-g accelerometers through data-driven methods

In the aerospace industry, pyroshock testing is an indispensable step in designing space electronics. Yet, damages in high-g accelerometers, the core measuring instruments in pyroshock test systems, could result in various failures of pyroshock tests. To ensure the reliability of pyroschock tests for space electronics, a machine learning system is proposed to perform self-validation for high-g accelerometers. In this work, self-validation refers to the capability of identifying five key parameters, namely the validated shock signal, the validated uncertainty, the measurement status, the raw shock signal and the fault type, synchronously during measuring shock signals in pyroshock tests. To achieve the highest performance, we accomplish these tasks through combining an ensemble learning model and a deep neural network (DNN). The ensemble learning model, which integrates several k-nearest neighbors with different k values, is used to identify the sensors’ health conditions from their measurements and diagnose their fault types synchronously if damaged. The DNN, a deep autoencoder-based neural network, is designed to correct corrupted measurements through constructing the mapping between faulty signals and their corresponding reference counterparts. Experimental results show that the proposed machine learning system is capable of not only accurately identifying the health conditions and fault types of the damaged high-g accelerometers from their measurements, but also recovering the corrupted shock signals to a large extent, and, meanwhile, outputting the five self-validation parameters.

Maskless lithography: an approach to SU-8 based sensitive and high-g Z-axis polymer MEMS accelerometer

Maskless lithography: an approach to SU-8 based sensitive and high-g Z-axis polymer MEMS accelerometer