Fault Diagnosis in Digital Part of Sigma-Delta Converter

Abstract

In this paper the artificial neural network (ANN) is applied to diagnosis of defects in the digital part of a nonlinear mixed-mode circuit. Both catastrophic and delay defects are considered. The approach is demonstrated on the example of a relatively complex sigma-delta modulator. Delay defects in this example are delays of rising and falling edge of digital signals and catastrophic defects are considered as stuck switches. Fault dictionary is created, by simulation, using the response of the circuit to an input ramp signal. It is represented in a form of a look-up table. Artificial neural network is then trained for modeling (memorizing) the look-up table. The diagnosis is performed so that the ANN is excited by faulty responses in order to present the fault codes at its output. There were no errors in identifying the faults during diagnosis.