Estimated Analysis of Radial Basis Function Neural Network for Induction Motor Fault Detection
Zareen J. Tamboli1, S.R.Khot2
1Zareen J. Tamboli, Dept of Electronics and Telecommunication, Sanjay Ghodawat’s Group of Institutes, Atigre, Kolhapur, India.
2S.R. Khot, Dept of Electronics and Telecommunication,, D.Y. Patil College of Engineering, Kolhapur, India.
Manuscript received on March 12, 2013. | Revised Manuscript received on April 17, 2013. | Manuscript published on April 30, 2013. | PP: 41-43 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1272042413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Faults in induction motors may cause a system to fail. Hence it is necessary to detect and correct them before the complete motor failure. In the paper, induction motors faults are studied and detected with the use of Radial basis function neural network. Radial Basis Function is trained and tested in this paper. Simple parameters like set of currents are taken as an input and fed to a Radial basis Function Neural Network. The comparison of different Radial Basis Functions is shown in this paper.
Keywords: Currents, Faults, Induction Motors, Radial Basis Function, Neural Network.