Study on several key problems in shock calibration of high-g accelerometers using Hopkinson bar

Yuan, Kai; Guo, Weiguo; Su, Yu; Shi, Yunbo; Lei, Jingyu; Guo, Hui

doi:10.1016/j.sna.2017.02.017

Cited by 20 publications

(4 citation statements)

References 8 publications

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“…However, measuring shock signal with high accuracy can be challenging with traditional accelerometers due to the extreme loading condition especially under the high-g shock environment [4]. While existing work in shock signal measurement is mainly based on more reliable but expensive hardware [5], in this paper we show that, high-g shock signals can be measured at a much lower cost with low-end sensors after deep learning calibration.…”

Section: Introductionmentioning

confidence: 81%

“…Traditional approaches to improve the sensor performance are usually based on designing better but more expensive hardware [10]. Existing research in calibrating shock sensors is mainly focused on making use of Hopkinson bar [5]. However, instead of directly calibrating the measured signals, Hopkinson bar is primarily used to calibrate the dynamic linearity, sensitivity, and repeatability of the accelerometer.…”

Section: Introductionmentioning

confidence: 99%

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Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Yao

Wen

Ren

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a dataset of shock signals measured by sensors of different fidelity. Secondly, we propose a novel network to effectively learn both the signal peak and overall shape. The results show that the proposed network is capable to map low-end shock signals to its high-end counterparts with satisfactory accuracy. To the best of our knowledge, this is the first work to apply deep learning techniques to calibrate shock sensors.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Yao

Wen

Ren

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Hopkinson pressure bar [4] has been widely used to evaluate the overload resistance of initiating explosive devices and calibrate high-gravitational acceleration sensors [5]. In the experiment of Hopkinson compression bar, the stress in the bar is a one-dimensional stress state [6,7].…”

Section: Introductionmentioning

confidence: 99%

Study on the influence of barrel venting on the overload waveform of projectile in bore

Zhao

Chao

Cao

et al. 2023

J. Phys.: Conf. Ser.

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In order to evaluate the anti shock overload ability of electronic components and parts in explosive device launcher, half sine wave generator with certain amplitude and pulse width is often used to verify it. In this paper, gun launcher is used as the shock wave generator. Different charge structures are studied for the interior ballistics process of barrel with or without venting according to certain requirements of mechanical overload. The influence of different charge structure, chamber volume and vent hole size on the overload waveform of projectile in the process of interior ballistic is studied by numerical simulation. The simulation results show that the larger the vent diameter is, the closer it is to the standard waveform. The research results can provide references for the matching design of projectile and artillery, the research and development of intelligent ammunition and the assessment. The research results can provide for the matching design of new artillery projectile and artillery and the research and development of intelligent ammunition.

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“…High-G accelerometers provide strong support for military and test in special environments. However, MEMS accelerometers often have large errors as part of the vibration measurement system, which reduces their practicability, the most significant of which is the relatively high level of self-noise observed in the output signal [ 2 , 6 ]. Noise limits the measurement resolution, so these sensors cannot be used for accurate diagnostic measurements [ 7 , 8 ].…”

Section: Introductionmentioning

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

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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.

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Study on several key problems in shock calibration of high-g accelerometers using Hopkinson bar

Cited by 20 publications

References 8 publications

Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Study on the influence of barrel venting on the overload waveform of projectile in bore

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

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