Raj, Krish K. and Kumar, Shahil and Cirrincione, Giansalvo and Cirrincione, Maurizio and Kumar, Rahul R. (2024) Bearing Fault Classification using Temporal Features for Wind Turbine Application: Harnessing Neural and Non - Neural Techniques. [Conference Proceedings]
Full text not available from this repository.Abstract
Wind turbines play a vital role in contemporary electrical grids as sources of renewable energy. Their continuous integration is essential for ensuring sustainable power generation, making the enhancement of turbine performance and longevity a top priority. Diagnosing bearing faults is critical but challenging due to complex vibration signals (nonlinear and non-stationary nature). In an effort to enhance the accuracy of bearing fault diagnosis, this study focused on identifying statistical time features (STFs) from run-to-failure vibration signal datasets. The research aimed to classify normal and three bearing fault variants using the STFs with principal component analysis (PCA). A comparative analysis was further conducted between neural and non-neural classifiers, employing 9 STFS firstly, then 5 reduced STFs by PCA for model training. Both the NN and non-NN models yielded superior results with the 9 STFs when compared to the PCA-transformed 5 STFS. Among the highest performing models, the Medium NN was chosen from the NN family classifiers and Ensemble Bagged Tree outperformed in its category of non-NN family architectures.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | Vibrations;Fault diagnosis;Training;Analytical models;Recurrent neural networks;Wind turbines;Principal component analysis;Principal Component Analysis;Neural Network;Bearing;Wind turbine;Fault Classification;Machine Learning;Statistical Time Features |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Rahul Kumar |
| Date Deposited: | 07 Mar 2024 23:27 |
| Last Modified: | 14 Mar 2024 22:50 |
| URI: | https://repository.usp.ac.fj/id/eprint/14465 |
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