Video-Based Vibration Analysis for Predictive Maintenance: A Motion Magnification and Random Forest Approach

Walid Gomaa, Walid Gomaa, Abdelrahman Wael Ammar, Ismael Abbo, Mohamed Galal Nassef, Tetsuji Ogawa, Mohab Hossam

2025

Abstract

Condition monitoring of high-speed machinery is critical to prevent unexpected breakdowns that could lead to injuries and cost billions. Traditional contact-based vibration sensors face limitations including measurement perturbations, point-specific data coverage, and installation constraints. This paper presents a novel non-contact machinery fault detection framework combining Eulerian video motion magnification with machine learning classification. The methodology comprises two integrated components. Primarily, a video-based vibration analysis pipeline utilizing Eulerian motion magnification with dense optical flow, which accomplish comprehensive signal processing for feature extraction using Fast Fourier transform. Then, a Random Forest classifier trained on video-derived temporal and frequency domain features. The system was validated based on ground-truth data from the Gunt PT500 machinery diagnosis and the Gunt TM170 balancing apparatus under four operational conditions: normal operation, outer ring bearing fault, and two imbalance severities (10g and 37g). Hence, experimental results demonstrate exceptional performance with 96.7% overall accuracy and a macro-averaged F1-score of 0.965 in discriminating fault conditions using solely video-derived features. The video processing allowed to identify distinct vibration signatures, from imbalance conditions showing amplitude variations to proportional fault severity ultimately offering a cost-effective solution for industrial condition monitoring applications.

Paper Citation

in Harvard Style

Gomaa W., Ammar A., Abbo I., Nassef M., Ogawa T. and Hossam M. (2025). Video-Based Vibration Analysis for Predictive Maintenance: A Motion Magnification and Random Forest Approach. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 445-452. DOI: 10.5220/0013715900003982

in Bibtex Style

@conference{icinco25,
author={Walid Gomaa and Abdelrahman Ammar and Ismael Abbo and Mohamed Nassef and Tetsuji Ogawa and Mohab Hossam},
title={Video-Based Vibration Analysis for Predictive Maintenance: A Motion Magnification and Random Forest Approach},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={445-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013715900003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Video-Based Vibration Analysis for Predictive Maintenance: A Motion Magnification and Random Forest Approach
SN - 978-989-758-770-2
AU - Gomaa W.
AU - Ammar A.
AU - Abbo I.
AU - Nassef M.
AU - Ogawa T.
AU - Hossam M.
PY - 2025
SP - 445
EP - 452
DO - 10.5220/0013715900003982
PB - SciTePress