Diagnostics and Prognostics Method for Analog Electronic Circuits

Abstract

Analog circuits play a vital role in ensuring the availability of industrial systems. Unexpected circuit failures in such systems during field operation can have severe implications. To address this concern, we developed a method for detecting faulty circuit condition, isolating fault locations, and predicting the remaining useful performance of analog circuits. Through the successive refinement of the circuit's response to a sweep signal, features are extracted for fault diagnosis. The fault diagnostics problem is posed and solved as a pattern recognition problem using kernel methods. From the extracted features, a fault indicator (FI) is developed for failure prognosis. Furthermore, an empirical model is developed based on the degradation trend exhibited by the FI. A particle filtering approach is used for model adaptation and RUP estimation. This method is completely automated and has the merit of implementation simplicity. Case studies on two analog filter circuits demonstrating this method are presented.