Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2018
Abstract
Vibration analysis is a key troubleshooting methodology for assessing the health of factory machinery. We propose an unobtrusive framework for at-a-distance visual estimation of such (possibly high frequency) vibrations, using a low fps (frames-per-second) camera that may, for example, be mounted on a worker's smart-glass. Our key innovation is to use an external stroboscopic light source (that, for example, may be provided by an assistive robot), to illuminate the machine with multiple mutually-prime strobing frequencies, and use the resulting aliased signals to efficiently estimate the different vibration frequencies via an enhanced version of the Chinese Remainder Theorem. Experimental results show that our technique estimates multiple such frequencies faster, and compares favourably to an equipment-mounted accelerometer alternative, with frequency estimation errors below 0.5% for vibrations occurring up to 500 Hz.
Keywords
Chinese Remainder Theorem, Frequency Estimation, Optical Sampling, Unobtrusive Multi-frequency Vibration Measurement
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Data Science and Engineering
Publication
IoPARTS '18: Proceedings of the 2018 International Workshop on Internet of People, Assistive Robots and ThingS: June 10, Munich, Germany
First Page
55
Last Page
59
ISBN
9781450358439
Identifier
10.1145/3215525.3215529
Publisher
ACM
City or Country
New York
Citation
ROY, Dibyendu; GHOSE, Avik; CHAKRAVARTY, Tapas; MUKHERJEE, Sushovan; PAL, Arpan; and MISRA, Archan.
Analysing multi-point multi-frequency machine vibrations using optical sampling. (2018). IoPARTS '18: Proceedings of the 2018 International Workshop on Internet of People, Assistive Robots and ThingS: June 10, Munich, Germany. 55-59.
Available at: https://ink.library.smu.edu.sg/sis_research/4368
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3215525.3215529