A Novel Deep Learning Approach for Automated Rolling Bearing Fault Diagnosis (ARBFD) Using Graph Neural Networks and Physics Informed Deep Learning

Karuppasamy L, Manivannan K, Kosalairaman T, Jeya Prasanna A, Kaviya R, Kaviya S

2025

Abstract

This work proposes a novel deep-learning method for automatic fault diagnosis in rolling bearings. The approach leverages the strengths of Graph Neural Networks (GNNs) for characteristic extraction and Physics-Informed Deep Learning (PIDL) to capture the underlying physics of bearing vibrations. Traditional strategies regularly depend on subjective and time- consuming expert evaluation. This information-pushed method overcomes those boundaries by at once classifying bearing fitness (every day or faulty) from raw vibration signals. The ARBFD method utilizes spectrograms, generated from vibration records, as entered into a pretrained GNN model. The GNN extracts informative functions from the spectrograms, which can be then fed right into a classifier for fault diagnosis. This mixture gives blessings: GNNs efficiently capture relationships within the spectrograms, while PIDL guarantees the model’s predictions are consistent with the physics of bearing faults. Experiments on a huge vibration dataset show the effectiveness of the ARBFD technique, reaching a classification accuracy of more than 95%. In addition, the technique outperforms conventional strategies and different deep-studying architectures. This method holds promise for actual-time, automatic tracking, and fault prognosis of rolling bearings, leading to progressed system reliability, decreased preservation costs, and prevention of sudden screw-ups in business packages. This work also contributes to the development of deep mastering for circumstance-based preservation and fault diagnosis in machinery, aligning with current research trends on applying GNNs for comparable obligations.

Paper Citation

in Harvard Style

L K., K M., T K., A J., R K. and S K. (2025). A Novel Deep Learning Approach for Automated Rolling Bearing Fault Diagnosis (ARBFD) Using Graph Neural Networks and Physics Informed Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 545-553. DOI: 10.5220/0013596500004664

in Bibtex Style

@conference{incoft25,
author={Karuppasamy L and Manivannan K and Kosalairaman T and Jeya Prasanna A and Kaviya R and Kaviya S},
title={A Novel Deep Learning Approach for Automated Rolling Bearing Fault Diagnosis (ARBFD) Using Graph Neural Networks and Physics Informed Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={545-553},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013596500004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - A Novel Deep Learning Approach for Automated Rolling Bearing Fault Diagnosis (ARBFD) Using Graph Neural Networks and Physics Informed Deep Learning
SN - 978-989-758-763-4
AU - L K.
AU - K M.
AU - T K.
AU - A J.
AU - R K.
AU - S K.
PY - 2025
SP - 545
EP - 553
DO - 10.5220/0013596500004664
PB - SciTePress