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

Yao, Houpu, Wen, Jingjing, Ren, Yi, Wu, Bin and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 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

Preview

PDF Download (1MB) | Preview

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.

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
URI:	https://orca.cardiff.ac.uk/id/eprint/120156

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item