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

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.

Automated summary

This paper investigates the approach of using testability analysis to build neural networks for fault diagnosis in DC-DC converters. The networks used are complex valued neural networks (CVNNs), which properly handle the phase and its information. Testability analysis is employed to effectively design the network and two methods considering the single-fault hypothesis are proposed. Theoretical foundations and practical examples are presented, and computer programs based on symbolic analysis techniques are used for analysis and training. The results obtained are highly satisfactory, demonstrating the optimal performance of the method.