Preprint Article Version 1 This version is not peer-reviewed

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.

Journal reference: Sensors 2018, 18, 2530
DOI: 10.3390/s18082530

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.

Subject Areas

vibration measurement; frequency prediction; deep learning; convolutional neural network; photogrammetry; computer vison; non-contact measurement

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.