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

He, Y., Tan, Y. and Sun, Y. (2003) Class-based neural network method for fault location of large-scale analogue circuits. In: Procs of the 2003 Int Symposium on Circuits and Systems : ISCAS '03. Institute of Electrical and Electronics Engineers (IEEE), pp. 733-736.
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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.


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