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Neural network-based analog fault diagnosis using testability analysis

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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.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Barbara Cannas, Alessandra Fanni & Augusto Montisci

  2. Department of Electronics and Telecommunications, University of Florence, Via S. Marta, 3, 50139, Firenze, Italy

    Stefano Manetti & Maria Cristina Piccirilli

Authors
  1. Barbara Cannas
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  2. Alessandra Fanni
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  3. Stefano Manetti
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  4. Augusto Montisci
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  5. Maria Cristina Piccirilli
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Corresponding author

Correspondence to Alessandra Fanni.

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Cannas, B., Fanni, A., Manetti, S. et al. Neural network-based analog fault diagnosis using testability analysis. Neural Comput & Applic 13, 288–298 (2004). https://doi.org/10.1007/s00521-004-0423-2

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  • DOI: https://doi.org/10.1007/s00521-004-0423-2

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