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

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3 Author(s)
Yigang He ; Dept. of Electron., Commun. & Electr. Eng., Hertfordshire Univ., Hatfield, UK ; Yanghong Tan ; Yichuang Sun

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.

Published in:

Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on  (Volume:5 )

Date of Conference:

25-28 May 2003