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In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine. [ABSTRACT FROM AUTHOR]

In this paper, a modified learning algorithm for the multilayer neural network with the multi-valued neurons (MLMVN) is presented. The MLMVN, which is a member of complex-valued neural networks family, has already demonstrated a number of important advantages over other techniques. A modified learning algorithm for this network is based on the introduction of an acceleration step, performing by means of the complex QR decomposition and on the new approach to calculation of the output neurons errors: they are calculated as the differences between the corresponding desired outputs and actual values of the weighted sums. These modifications significantly improve the existing derivative-free backpropagation learning algorithm for the MLMVN in terms of learning speed. A modified learning algorithm requires two orders of magnitude lower number of training epochs and less time for its convergence when compared with the existing learning algorithm. Good performance is confirmed not only by the much quicker convergence of the learning algorithm, but also by the compatible or even higher classification/prediction accuracy, which is obtained by testing over some benchmarks (Mackey-Glass and Jenkins-Box time series) and over some satellite spectral data examined in a comparison test. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits. [ABSTRACT FROM AUTHOR]