Hierarchical approach to diagnosis of electronic circuits using ANNs

In this paper, we apply artificial neural networks (ANNs) to the diagnosis of a mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating the fault code and the circuit response for a given input signal. Also, hypercomputing was implemented, i.e. we used parallel simulation of large number of replicas of the original circuit with faults inserted to achieve fast creation of the fault dictionary. ANNs were used to model the fault dictionaries. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for searching the fault dictionary. A voting system was created at the topmost level in order to distinguish which ANN output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter.