Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration
This work addresses the cost barrier for high-accuracy shock measurement in industrial applications, though it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackled the problem of measuring industrial shock signals accurately without expensive high-end sensors by using deep neural networks to calibrate cheap low-end sensors, achieving satisfactory accuracy in mapping low-end to high-end signals.
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.