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Low-cost measurement of industrial shock signals via deep learning calibration

Yao, Houpu, Wen, Jingjing, Ren, Yi, Wu, Bin and Ji, Ze ORCID: 2019. Low-cost measurement of industrial shock signals via deep learning calibration. Presented at: International Conference on Acoustics, Speech, and Signal Processing, Brighton, UK, 12-17 May 2019. ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp. 2892-2896. 10.1109/ICASSP.2019.8682484

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher: IEEE
ISBN: 9781479981311
Date of Acceptance: 1 February 2019
Last Modified: 25 Oct 2022 13:34

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