A novel identification technique for the characterization of an electrical apparatus is presented. The approach is based on two multi-valued neuron neural networks operating in a joined architecture able to extract geometrical displacements or insulation changes, not directly accessible or measurable. The inputs of the neural system are the samples of the frequency response on a given significant band, while the outputs represent the estimation of one or more geometrical parameters or other features related to the material properties. The method uses a Frequency Response Analysis (FRA) approach in order to preprocess the data to present to the net.

A double neural network for interpretation of the frequency response in the electrical equipments / F. Grasso; A. Luchetta; S. Manetti; M. C. Piccirilli. - ELETTRONICO. - (2012), pp. 226-231. (Intervento presentato al convegno 6th IEEE International Conference on Intelligent Systems (IS 2012) tenutosi a Sofia, Bulgaria nel settembre 2012) [10.1109/IS.2012.6335140].

A double neural network for interpretation of the frequency response in the electrical equipments

GRASSO, FRANCESCO;LUCHETTA, ANTONIO;MANETTI, STEFANO;PICCIRILLI, MARIA CRISTINA
2012

Abstract

A novel identification technique for the characterization of an electrical apparatus is presented. The approach is based on two multi-valued neuron neural networks operating in a joined architecture able to extract geometrical displacements or insulation changes, not directly accessible or measurable. The inputs of the neural system are the samples of the frequency response on a given significant band, while the outputs represent the estimation of one or more geometrical parameters or other features related to the material properties. The method uses a Frequency Response Analysis (FRA) approach in order to preprocess the data to present to the net.
2012
Intelligent Systems (IS), 2012 6th IEEE International Conference
6th IEEE International Conference on Intelligent Systems (IS 2012)
Sofia, Bulgaria
settembre 2012
F. Grasso; A. Luchetta; S. Manetti; M. C. Piccirilli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/777695
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