A nonlinear dynamic model and parameters identification method for predicting the shock pulse of rubber waveform generator

Wen, Jingjing; Chengwu, Liu; Yao, Houpu; Wu, Bin

doi:10.1016/j.ijimpeng.2018.05.009

Cited by 21 publications

(17 citation statements)

References 30 publications

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“…We conduct drop shock test to collect shock signals. The experiment platform setting is based on our previous work in [17], which is also illustrated in Fig.1. The logic behind this drop shock test is simple: the drop table is lifted up and released, it falls freely along the guide columns and collides with the rubber waveform generator (RWG) to produce the shock signal.…”

Section: Data Collectionmentioning

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: Data Collectionmentioning

confidence: 99%

“…Illustration of the drop test platform. Sensors are marked as "Accelerometer" mounted on the top of the drop table.Figure adaptedfrom[17].…”

mentioning

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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“…hock loading, especially the high-g level shock, is characterized as a transient transfer of massive energy to a system, which is very likely to seriously affect the performance of the system [1]. Many conditions, such as the release of space equipment [2], drop of electronic devices [3], and crashing of vehicles [4], are subjected to high-g shock excitations.…”

Section: Introductionmentioning

confidence: 99%

“…Lastly, these mapping methods cannot calibrate the entire time-domain shock signal but merely the peak value and/or pulse width of a shock signal [14]. Only the two parameters (peak value and pulse width), though they are two key parameters in conventional standard-waveform-based shock test [1], cannot describe the actual shock environment exactly. The shock response spectrum (SRS) method, which converts the entire time-domain shock signal into a frequency-domain spectrum, is proposed for signal characterization and replacing the standard-waveform-based shock test methods gradually, especially in pyroshock test [2], [6].…”

Section: Introductionmentioning

confidence: 99%

“…The pipeline of our work is as follows: Firstly, an experimental high-g shock environment is developed by combining a drop shock tester [1] and a dual mass shock amplifier (DMSA) [3]. This setup is pneumatic-driven with high operating efficiency, which is particularly beneficial for deep learning methods due to the low time-cost in generating and collecting abundant shock signal data.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

Wen

Yao

et al. 2020

IEEE Sensors J.

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A deep learning based approach is proposed to accurately recover shock signals measured from a damaged high-g accelerometer without modifying the hardware. We first conducted shock tests and collected a large dataset of shock signals with different levels of acceleration by using an efficient experimental apparatus. The training data is composed of a pair of signals simultaneously obtained from a faulty accelerometer and a high-end accelerometer (served as the ground truth). A customized autoencoder neural network is designed and trained on this dataset, aiming to map the faulty signals to their reference counterparts. Experimental results show that, with the help of deep learning, shock signals can be accurately recovered from the faulty measurements. Compared with conventional approaches that require diagnosing and replacing faulty parts, the proposed data-driven method demonstrates a highly promising solution that allows recovering corrupted signals without introducing extra work to upgrade the hardware at almost zero cost. The dataset and code of this work are made publicly available on GitHub at https://github.com/hope-yao/Sensor_Calibration.

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