Fault Diagnosis of Linear Analog Electronic Circuit Based on Natural Response Specification using K-NN Algorithm

This paper reports a novel method for parametric fault diagnosis in linear analog electronic circuits using distance weighted cosine K-Nearest Neighbours (K-NN) algorithm that performs data classification on the basis of cosine similarity between data features or attributes. In this approach the analog electronic Circuit Under Test (CUT) is represented in the form of a transfer function model and natural response specifications of the system such as damping ratio, natural frequency and static gain of the system are extracted as features from this model. For experimentation purpose a second order Sallen-Key band pass filter and a fourth order Chebychev Type 1 low pass filter is considered, the corresponding fault classes are created for each of the circuit. The parameter values of the passive components in the system have been varied to derive the features, and each component whose tolerance varied is labelled with a corresponding fault class. The proposed methodology classifies faulty classes with accuracy greater than 95%.