Neural network-based analog fault diagnosis using testability analysis

Authors:
Barbara Cannas;Alessandra Fanni;Stefano Manetti;Augusto Montisci;Cristina Piccirilli
Affiliations:
University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy;University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy;University of Florence, Department of Electronics and Telecommunications, Via S. Marta, 3, 50139, Firenze, Italy;University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy;University of Florence, Department of Electronics and Telecommunications, Via S. Marta, 3, 50139, Firenze, Italy
Venue:
Neural Computing and Applications
Year:
2004

Citing 0
Cited 2

Ensembles of Neural Networks for Fault Diagnosis in Analog Circuits

Journal of Electronic Testing: Theory and Applications
Algebraic approach to ambiguity-group determination in nonlinear analog circuits

IEEE Transactions on Circuits and Systems Part I: Regular Papers

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Abstract

A fault diagnosis procedure for analog linear circuits is presented. It uses an off-line trained neural network as a classifier. The innovative aspect of the proposed approach is the way the information provided by testability and ambiguity group determination is exploited when choosing the neural network architecture. The effectiveness of the proposed approach is shown by comparing with similar work that has already appeared in the literature.