Authors:
Edicson Diaz
1
;
2
;
Enrique Naredo
3
;
Nicolas Díaz
3
;
Douglas Dias
4
;
Maria Diaz
2
;
5
;
Susan Harnett
5
and
Conor Ryan
4
Affiliations:
1
Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
;
2
Eastway Reliability, Limerick, Ireland
;
3
Universidad del Caribe, Cancun, Mexico
;
4
Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland
;
5
School of Engineering, University of Limerick, Limerick, Ireland
Keyword(s):
Bearing Fault Classification, Vibration Analysis, Neural Architecture Search, Hyperparameter Optimization.
Abstract:
In this research, we address bearing fault classification by evaluating three neural network models: 1D Con-volutional Neural Network (1D-CNN), CNN-Visual Geometry Group (CNN-VGG), and Long Short-Term Memory (LSTM). Utilizing vibration data, our approach incorporates data augmentation to address the limited availability of fault class data. A significant aspect of our methodology is the application of neural architecture search (NAS), which automates the evolution of network architectures, including hyperparameter tuning, significantly enhancing model training. Our use of early stopping strategies effectively prevents overfitting, ensuring robust model generalization. The results highlight the potential of integrating advanced machine learning models with NAS in bearing fault classification and suggest possibilities for further improvements, particularly in model differentiation for specific fault classes.