Class-based neural network method for fault location of large-scale analogue circuits

Y. He, Y. Tan, Y. Sun

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Citations (Scopus)
    17 Downloads (Pure)

    Abstract

    A new method for fault diagnosis of large-scale analogue circuits based on the class concept is developed in this paper. A large analogue circuit is decomposed into blocks/sub-circuits and the nodes between the blocks are classified into three classes. Only those sub-circuits related to the faulty class need to be treated. Node classification reduces the scope of search for faults, thus reduced after-test time. The proposed method is more suitable for real-time testing and can deal with both hard and soft faults. Tolerance effects are taken into account in the method. The class-based fault diagnosis principle and neural network based method are described in some details. Two non-trivial circuit examples are presented, showing that the proposed method is feasible.
    Original languageEnglish
    Title of host publicationProcs of the 2003 Int Symposium on Circuits and Systems
    Subtitle of host publicationISCAS '03
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages733-736
    Volume5
    DOIs
    Publication statusPublished - 2003

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