Kumar, Rahul R. and Cirrincione, Giansalvo and Taherzadeh, Mehdi and Hénao, Humberto (2023) Open Rotor Phase Fault Detection in Wound - Rotor Induction Machines Using Signal Texture and Shallow Neural Networks. [Conference Proceedings]
Full text not available from this repository.Abstract
Studies related to fault detection in induction motors have taken a next step as machine learning techniques are becoming popular as the industries adapt to Industry 4.0 and make provisions for Industry 5.0. In relation to that, this paper proposes a texture-based feature estimation coupled with a shallow neural network for detection of open rotor phase in wound rotor induction machines using only 3-phase current signals. After signal conditioning of the acquired experimental data and calculation of contrast definitions (texture-based), optimized shallow multi-layer perceptron neural networks have emerged to be the best classification model with respect to its other neural variants. The model selection has been done based on overall architecture, classification accuracy, confidence in probability predictions, time complexity and least number of trainable parameters.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | Industries;Fault detection;Windings;Rotors;Stator windings;Predictive models;Feature extraction;Contrast;Optimization;Induction Motors;Artificial Intelligence;Texture;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 22:03 |
| Last Modified: | 07 Mar 2024 22:03 |
| URI: | https://repository.usp.ac.fj/id/eprint/14457 |
Actions (login required)
 |
View Item |