This paper presents a data driven approach where at first the most significant features of the three phase current signal are analyzed and then a Curvilinear Component based analysis (CCA), which is a nonlinear manifold learning technique, is performed to compress and interpret the feature behaviour. Finally, a multi-layer perceptron network is used to develop a classifier. The effectiveness of the developed model is verified experimentally with data provided on-line and in real-time.

A Topological and Neural Based Technique for Classification of Faults in Induction Machines

Kumar, Rahul Ranjeev
;
Cirrincione, G
;
Tortella, A
;
Andriollo, M
2018

Abstract

This paper presents a data driven approach where at first the most significant features of the three phase current signal are analyzed and then a Curvilinear Component based analysis (CCA), which is a nonlinear manifold learning technique, is performed to compress and interpret the feature behaviour. Finally, a multi-layer perceptron network is used to develop a classifier. The effectiveness of the developed model is verified experimentally with data provided on-line and in real-time.
2018
IEEE Xplore Digital Library
2018 21st International Conference on Electrical Machines and Systems (ICEMS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3333723
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