Article
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Learning to See the Vibration: a Neural Network for Vibration Frequency Prediction
Version 1
: Received: 4 July 2018 / Approved: 5 July 2018 / Online: 5 July 2018 (08:31:00 CEST)
A peer-reviewed article of this Preprint also exists.
Liu, J.; Yang, X. Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction. Sensors 2018, 18, 2530. Liu, J.; Yang, X. Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction. Sensors 2018, 18, 2530.
Abstract
Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.
Keywords
vibration measurement; frequency prediction; deep learning; convolutional neural network; photogrammetry; computer vison; non-contact measurement
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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