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JRM Vol.34 No.5 pp. 1011-1023
doi: 10.20965/jrm.2022.p1011
(2022)

Paper:

Real-Time Vibration Visualization Using GPU-Based High-Speed Vision

Feiyue Wang*, Shaopeng Hu*, Kohei Shimasaki**, and Idaku Ishii*

*Graduate School of Advanced Science and Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan

**Digital Monozukuri (Manufacturing) Education and Research Center, Hiroshima University
3-10-32 Kagamiyama, Higashi-hiroshima, Hiroshima 739-0046, Japan

Received:
April 7, 2022
Accepted:
July 1, 2022
Published:
October 20, 2022
Keywords:
vibration monitoring, high-speed vision, digital image correlation, short-time Fourier transform
Abstract

In this study, we developed a real-time vibration visualization system that can estimate and display vibration distributions at all frequencies in real time through parallel implementation of subpixel digital image correlation (DIC) computations with short-time Fourier transforms on a GPU-based high-speed vision platform. To help operators intuitively monitor high-speed motion, we introduced a two-step framework of high-speed video processing to obtain vibration distributions at hundreds of hertz and video conversion processing for the visualization of vibration distribution at dozens of hertz. The proposed system can estimate the full-field vibration displacements of 1920 × 1080 images in real time at 1000 fps and display their frequency responses in the range of 0–500 Hz on a computer at dozens of frames per second by accelerating phase-only DICs for full-field displacement measurement and video conversion. The effectiveness of this system for real-time vibration monitoring and visualization was demonstrated by conducting experiments on objects vibrating at dozens or hundreds of hertz.

Real-time visualization of vibration using high-speed vision

Real-time visualization of vibration using high-speed vision

Cite this article as:
F. Wang, S. Hu, K. Shimasaki, and I. Ishii, “Real-Time Vibration Visualization Using GPU-Based High-Speed Vision,” J. Robot. Mechatron., Vol.34 No.5, pp. 1011-1023, 2022.
Data files:
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