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arXiv · 1902.02829

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

Houpu Yao·Jingjing Wen·Yi Ren·Bin Wu·Ze Ji
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Abstract

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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Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji. 2019-02-07. Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration. https://arxiv.org/abs/1902.02829

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