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.
@article{arxiv.1902.02829,
title = {Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration},
author = {Houpu Yao and Jingjing Wen and Yi Ren and Bin Wu and Ze Ji},
journal= {arXiv preprint arXiv:1902.02829},
year = {2019}
}