Output Signal Of Accelerometer Research Articles

A Temperature Compensation Approach for Micro-Electro-Mechanical Systems Accelerometer Based on Gated Recurrent Unit-Attention and Robust Local Mean Decomposition-Sample Entropy-Time-Frequency Peak Filtering.

MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer's output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11%, with values of 0.23032 g/h/Hz for the original accelerometer output signal and 0.00895695 g/h/Hz for the processed signal.

RESULTS OF SEMI-NATURAL TESTS OF THE AUTONOMOUS DETERMINATION OF THE VEHICLE LONGITUDE AND LATITUDE

Navigation parameters such as vehicle heading, linear speed and latitude and longitude are determined today, as a rule,using satellite navigation systems. However, there is a need for autonomous determination of navigation parameterswhen satellite navigation system signals are unavailable for one reason or another. For example, satellite navigationsystem signals can be jammed by electromagnetic countermeasures.Autonomous systems for determining the vehicle navigation parameters are inertial navigation systems, the basis ofwhich is the double integration of signals from accelerometers, as well as the integration of signals from gyroscopes toform one or another coordinate system, if we are talking about strapdown inertial navigation systems (SINS).Integration of the output signals of accelerometers and gyroscopes, which contain errors, leads to the accumulation ofSINS errors. To increase accuracy, SINS are integrated, for example, with satellite navigation systems. However, suchsystems cease to be completely autonomous.The article uses the method of autonomous determination of vehicle latitude and longitude, which is a new method ininertial navigation. The method is based on the strapdown inertial technology, which, unlike the standard methods usedin SINS, does not use the integration of the output signals of accelerometers and gyroscopes. To determine the angles oflatitude and longitude, the signals of the gyroscopes included in the inertial measurement unite (IMU) are used.As a result of a semi-natural experiment, the efficiency of the method of autonomous determination of latitude andlongitude was verified. Flight data of a small-sized aircraft were used for experimental verification. The data containedinformation about the angles of the heading, pitch, roll of the aircraft, the output signals of three micromechanicalgyroscopes, three micromechanical accelerometers, information about latitude, longitude and altitude measured by thereceiver of the satellite navigation system, as well as information about the horizontal and vertical components of speedand current time. Plots of calculated values of latitude and longitude and values measured using the receiver of thesatellite navigation system are given. From the given plots and tables, it can be seen that from a certain time, thecalculated values coincide with the "reference" ones, which confirms the efficiency of the method of autonomousdetermination of latitude and longitude.

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.

A New Joint Denoising Algorithm for High‐G Calibration of MEMS Accelerometer Based on VMD‐PE‐Wavelet Threshold

Recently, the High‐G MEMS accelerometer (HGMA) has been used in navigation, mechanical property detection, consumer electronics, and other fields widely. As the core component of a measuring system, it is very crucial to enhance the calibration accuracy of the accelerometer. In order to remove the noises in the accelerometer output signals to enhance its calibration accuracy, a combined denoising method which combines variational mode decomposition (VMD) with permutation entropy (PE) and wavelet threshold is given in this article. For the sake of overcoming the defect of signal distortion caused by the traditional denoising methods, this joint denoising method combines the good decomposition characteristics of VMD and the good denoising ability of wavelet threshold and introduces PE as a judgment criterion to achieve a good balance between denoising effect and signal fidelity. The combination of PE and VMD not only avoids the phenomenon of mode aliasing but also improves the ability to identify the noise components, which makes the wavelet threshold denoising more specific. Firstly, some intrinsic mode functions (IMFs) are obtained by using VMD to decompose the complex signal containing noise which is outputted from the accelerometer. Secondly, the IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE algorithm. Thirdly, the noise IMF components can be discarded directly, and then the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained. Finally, the final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and the useful IMF components retained before denoising. The experimental results prove that the combined denoising algorithm combines the merits of VMD, PE, and wavelet threshold, and this new algorithm has a good performance in the calibration denoising of accelerometer. Compared with the serious signal distortion caused by using only EMD or wavelet threshold, this method not only has a good denoising effect (the noises in the static part are eliminated by 99.97% and the SNR of the dynamic part is raised to 18.56) but also can maintain a good signal fidelity (the error of shock peak amplitude is 3.4%, the error of vibration peak amplitude is 0.4%, and the correlation coefficient between the denoising signals and dynamic part is as high as 0.982).

Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition.

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

Research on adaptive modulus maxima selection of wavelet modulus maxima denoising

Microelectromechanical system (MEMS) accelerometers are small in size, low in power consumption and easily integrated. They can be used in intelligent hydraulic components to obtain the dynamic acceleration of the system and monitor the operating status of the system. In this study, based on the noise characteristics of MEMS accelerometer output signals, a wavelet modulus maxima denoising algorithm based on adaptive threshold estimation is proposed, in which SureShrink threshold estimation is used to choose the right modulus maxima. Then, the signal-to-noise ratio and mean-square-error are used as the evaluating indices of the denoising performance for the wavelet modulus maxima denoising based on SureShrink threshold estimation, the wavelet modulus maxima denoising based on BayesShrink threshold estimation and the normal wavelet modulus maxima denoising. The simulation results show that the wavelet modulus maxima denoising based on SureShrink threshold estimation has better denoising performance than the normal modulus maxima denoising and the wavelet modulus maxima method based on BayesShrink threshold estimation, and effectively eliminates the noise of MEMS accelerometer output signals.

An Adaptive Dual-Window Step Detection Method for a Waist-Worn Inertial Navigation System

In waist-worn pedestrian navigation systems, the periodic vertical acceleration peak signal at body centre of gravity is widely used for detecting steps. Due to vibration and waist shaking interference, accelerometer output signals contain false peaks and thus reduce step detection accuracy. This paper analyses the relationship between periodic acceleration at pedestrian centre of gravity and walking stance during walking. An adaptive dual-window step detection method is proposed based on this analysis. The peak signal is detected by a dual-window and the window length is adjusted according to the change in step frequency. The adaptive dual window approach is shown to successfully suppress the effects of vibration and waist shaking, thereby improving the step detection accuracy. The effectiveness of this method is demonstrated through step detection experiments and pedestrian navigation positioning experiments respectively. The step detection error rate was found to be less than 0·15% in repeated experiments consisting of 345 steps, while the longer (about 1·3 km) pedestrian navigation experiments demonstrated typical positioning error was around 0·67% of the distance travelled.

Identification of scale factor of a pendulum compensating accelerometer during an orbital spacecraft mission

The paper deals with a crucial task of estimating the metrological characteristics of a pendulum accelerometer which change under the influence of perturbations in the flight of spacecraft. A method is developed that makes it possible to estimate the value of the scale factor of a compensating pendulum accelerometer in the system of spacecraft maneuver control through the use of grapho-analytical and indirect methods of diagnosing first and second order systems. The proposed method makes possible automatic identification of the scale factor in the absence of external forces of non-gravitational origin. The essence of the method is based on the signal-extracting impact on the actuator accelerometer which causes a natural deviation of the sensing element, and the analysis of the transient response of the accelerometer output signal. Numerical studies show that the accuracy of estimating the scale factor in accordance with the proposed method depends on three factors: the output of the measuring noise, the attenuation coefficient of the pendulum accelerometer sensor, and the moments of the external forces acting on the sensitive axis of the accelerometer in the identification process. The results of simulation led to the conclusion about the possibility of applying this method in solving the problem of self-control of the characteristics of a pendulum accelerometer. It is shown that an increase in the accuracy of estimating the scale factor is possible due to the use of statistical methods of smoothing and filtering. Preliminary analysis showed that the proposed method can be implemented in the creation of algorithmic software of the self-control system of smart accelerometers.

Random error modelling and compensation of accelerometer in airborne remote sensing stabilized platform

This paper studies the impact of the accelerometer output error on the levelling accuracy in levelling mode of the airborne remote sensing stabilized platform. On the basis that the accelerometer output signal is non-stationary, a non-stationary time series model of ARIMA(3,0,1) is established, with which an adaptive Kalman filter is designed. In the Kalman filter, an online correction method for the forgetting factor based on real-time measurements is presented, achieving the automatic adjustment of the Kalman filter gain. The model of the accelerometer and the adaptive Kalman filter are applied to the airborne remote sensing stabilized platform principle prototype made by our research group. On one hand, the results show that the model is fit for the accelerometer. On the other hand, it is noted that the new filter has improved the measurement accuracy of the accelerometer, depressed the oscillations of the levelling progress of the platform and reduced the steady-state error of the platform. The levelling performance of the stabilized platform is efficiently improved after all these work.

가속도계를 이용한 마이크로스폿용접의 인프로세스 모니터링

This study is to propose an in-process monitoring system for micro resistance spot welding processes using minute accelerometer. A minute accelerometer is mounted on the upper moving electrode tip holder. With its high sensitivity and frequency response characteristics, accelerometer output signal has been successfully recorded and integrated twice to reflect electrode expansion during micro spot welding processes. The analysis of electrode expansion pattern was attempted to find its correlation with spot weld quality. Major previous findings1-6) regarding spot weld quality assessment with the electrode expansion signal in large scale resistance spot welding processes were proved to be true in this in-process monitoring system.

Interaction of a shock with a sphere suspended in a vertical shock tube

Shock wave interaction with a sphere is one of the benchmark tests in shock dynamics. However, unlike wind tunnel experiments, unsteady drag force on a sphere installed in a shock tube have not been measured quantitatively. This paper presents an experimental and numerical study of the unsteady drag force acting on a 80 mm diameter sphere which was vertically suspended in a 300 mm x 300 mm vertical shock tube and loaded with a planar shock wave of M s = 1.22 in air. The drag force history on the sphere was measured by an accelerometer installed in it. Accelerometer output signals were subjected to deconvolution data processing, producing a drag history comparable to that obtained by solving numerically the Navier-Stokes equations. A good agreement was obtained between the measured and computed drag force histories. In order to interpret the interaction of shock wave over the sphere, high speed video recordings and double exposure holographic interferometric observations were also conducted. It was found that the maximum drag force appeared not at the time instant when the shock arrived at the equator of the sphere, but at some earlier time before the transition of the reflected shock wave from regular to Mach reflection took place. A negative value of the drag force was observed, even though for a very short duration of time, when the Mach stem of the transmitted shock wave relfected and focused at the rear stagnation point of the sphere.

Packaging effects on the performances of MEMS for high-G accelerometer with double-cantilevers

Abstract In this work, finite element simulation has been applied in frequency-domain and time-domain analyses for a packaged accelerometer used in high-G environments. In order to understand the effect of the adhesive material, which is filled in the gap between the sensor chip and the package bulk, on the output signal of accelerometer, a simplified packaged structure has been adopted in this analysis. The results from simulations show that the Young’s moduli of seal adhesive has important influences on the mode shapes of the packaged accelerometer and a small Young’s modulus will bring in strong distortion of output signal of accelerometer. As the Young’s moduli increased, the waveform of accelerometer output signal became better after digital filtering. The seal adhesive with Young’s modulus like molding compound in IC packaging would not induce significant problems in signal distortion. The frequency and averaged amplitude of output signal of the packaged accelerometer were not affected by Young’s modulus of seal material. The accelerometer output was linearly dependent on the input of acceleration impulse and the response was very well when the input of the acceleration amplitudes changed. The simulations for a practical packaging structure defined in this work showed that the results were similar to the simulations for the simplified structure of accelerometer with packaging, and the simulation results from the simplified structure could serve as a basis for design of high-G accelerometer packaging in a certain degree.

Acceleration signal characteristics for loose part impact.

Characteristics of acceleration frequency spectra for an impact signal were studied and loose part mass was estimated, using variation in the frequency spectral patterns. The frequency spectral pattern for the acceleration induced by impact depends on the loose part mass and not on the impact energy and the loose part shape. It was found that the frequency spectral pattern for an impact which occurred at the lower plenum vessel wall is not influenced by the impact point. On the other hand, the maximum acceleration amplitude depends not only on the loose part mass but also on the impact energy, the loose part shape and the distance from the impact point. It was also found that the foremost portion of the accelerometer output signal is influenced by the impact force between a loose part and the vessel. However, a second portion is influeuced by the sensor resonance and structural resonances. Loose part mass estimation, using “FR” values (ratio between low frequency component and high frequency component), is useful. The uncertainty range for the estimated loose part mass is about 0.7 decade.

Monitoring structural vibrations inside reactor vessels

The proposed method continuously or periodically monitors the vibration of in-reactor elements and observe changes in the relative frequency and amplitude of vibrations of each individual structural element. This makes it possible to prevent any possible mechanical damage to in-reactor elements. An advantage of the proposed vibrations monitoring method is the possibility of suppressing the undesirable effect of various acoustic interference of a technological nature which practically disappear at frequencies above 100 kHz. This can be achieved by selecting a suitable operating frequencies of ultrasonic radiators and ensuring high selectivity of the receiving channels. An important drawback of the proposed method is the presence of low-frequency amplitude modulation of the field set up by radiator caused by coolant temperature fluctuations. Additional physical information is needed to suppress this interference and to ensure reliable detection of the field modulation component associated with vibrations of a specific in-reactor element. Such information can by provided by noise signals of pressure and neutron flux transducers (both inside and outside the core) and by output signals of accelerometers mounted on outside surface of the reactor vessel and monitoring its vibrations.

Vibration and Acoustic Tests of Missile Structure

The results of vibration and acoustic tests of a missile structure are presented. Sinusoidal vibration surveys, low-level acoustic tests using a random siren as the noise source, and high-intensity random-noise tests to determine the probability of acoustically induced structural failures during underground launch operations were conducted. Resonance frequencies, mode shapes, and damping ratios were determined, and the absolute value of the driving point impedance as a function of frequency was calculated. The correlation between localized excitation (random siren) and excitation of the entire structure during an in-silo launch was investigated. The linearity of acceleration response of skin panels, stringers, and ring frames was determined from 13-octave band analysis of the output signals of accelerometers and of microphones over a range of over-all sound-pressure levels from 140 to 165 db.