Fuzzy classifier for fault diagnosis in analog electronic circuits.

Many studies have presented different approaches for the fault diagnosis with fault models having ± 50% variation in the component values in analog electronic circuits. There is still a need of the approaches which provide the fault diagnosis with the variation in the component value below ± 50%. A new single and multiple fault diagnosis technique for soft faults in analog electronic circuit using fuzzy classifier has been proposed in this paper. This technique uses the simulation before test (SBT) approach by analyzing the frequency response of the analog circuit under faulty and fault free conditions. Three signature parameters peak gain, frequency and phase associated with peak gain, of the frequency response of the analog circuit are observed and extracted such that they give unique values for faulty and fault free configuration of the circuit. The single and double fault models with the component variations from ± 10% to ± 50% are considered. The fuzzy classifier along the classification of faults gives the estimated component value under faulty and faultfree conditions. The proposed method is validated using simulated data and the real time data for a benchmark analog circuit. The comparative analysis is also presented for both the validations.