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

## TL;DR

This paper demonstrates that low-cost sensors, when calibrated with deep neural networks, can accurately measure high-g shock signals, offering a cost-effective alternative to expensive high-end sensors.

## Contribution

It introduces a novel deep learning approach to calibrate low-end shock sensors, enabling accurate high-g shock measurement without high-end hardware.

## Key findings

- Deep neural networks effectively calibrate low-end sensors.
- The method accurately maps low-end signals to high-end counterparts.
- First application of deep learning for shock sensor calibration.

## 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.