Chand, Prasheel and Mani, Ishant and Kumar, Rahul R. and Cirrincione, Maurizio (2024) Machine Learning-Based Classification of Stator Inter - Turn Faults Severities Using Time Domain Features and Principal Component Analysis. [Conference Proceedings]
Full text not available from this repository.Abstract
Stator-based faults are prevalent in Induction Motors (IMs) across various industries, including the electric utility sector. To enhance the safety and reliability of IMs, condition monitoring and fault classification play a crucial role. This paper proposes a data-driven approach for classifying Stator Inter Turn Faults (SITF) severities in IMs using 3-phase stator current signals. The application of Principal Component Analysis (PCA) on the time domain features is also demonstrated, yielding promising results that showcase the potential of a Narrow Neural Network (NN) for accurate SITF severity classification. Particularly, the narrow neural network classifier showed promising results with a classification accuracy of 98.23% on test-set experimental data. This study explores innovative fault classification methods, leading to advancements in component-level diagnostics and enhancing reliability. It lays the foundation for more efficient and reliable preventative maintenance strategies.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | Artificial neural networks;Stators;Real-time systems;Reliability;Time-domain analysis;Transient analysis;Principal component analysis;induction motors;stator fault;statistical time domain features;principle component analysis;dimensionality reduction;neural networks |
| 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 23:22 |
| Last Modified: | 07 Mar 2024 23:22 |
| URI: | https://repository.usp.ac.fj/id/eprint/14464 |
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