DC-DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC-DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method.

Testability Evaluation in Time-Variant Circuits: A New Graphical Method / Bindi, M; Piccirilli, MC; Luchetta, A; Grasso, F; Manetti, S. - In: ELECTRONICS. - ISSN 2079-9292. - ELETTRONICO. - 11:(2022), pp. 1589-1613. [10.3390/electronics11101589]

Testability Evaluation in Time-Variant Circuits: A New Graphical Method

Bindi, M;Piccirilli, MC;Luchetta, A;Grasso, F;Manetti, S
2022

Abstract

DC-DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC-DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method.
11
1589
1613
Bindi, M; Piccirilli, MC; Luchetta, A; Grasso, F; Manetti, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1282902
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