Kumar, Rahul R. and Randazzo, Vincenzo and Cirrincione, G. and Cirrincione, Maurizio and Pasero, Eros (2017) Analysis of stator faults in induction machines using growing curvilinear component analysis. [Conference Proceedings]
Full text not available from this repository. (Request a copy)Abstract
Fault detection of shorted turns in the stator windings of Induction Motors (IMs) is possible in a variety of ways. As current sensors are usually installed together with the IMs for control and protection purposes, using stator current for fault detection has become a common practice nowadays, as it is much cheaper than installing additional sensors. In this study, stator currents from the healthy and faulty IMs are obtained and analysed via MATLAB® software. The current signatures from healthy and faulty IMs are conditioned using the inbuilt DSP module of the dSPACE prior to analysis using AI techniques. This paper presents a Growing Curvilinear Component Analysis (GCCA) neural network which is able to correctly identify anomalies in the IM and follow the evolution of the stator fault using its current signature, making on-line early fault detection possible.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 17 Oct 2018 06:40 |
| Last Modified: | 24 Nov 2022 23:54 |
| URI: | https://repository.usp.ac.fj/id/eprint/11114 |
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