IEEE Transactions on Industrial Electronics, Vol 60, 2012.

Diagnostics and Prognostics Method for Analog Electronic Circuits

Arvind Sai Sarathi Vasan1, Student Member, IEEE, Bing Long2 and Michael Pecht1, Fellow, IEEE

1Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland, College Park, MD 20742, USA

2School of Automation Engineering,
University of Electronic Science and Technology of China, Chengdu, China


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 successive refinement of 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 is developed for failure prognosis. Further, an empirical model is developed based on the degradation trend exhibited by the fault indicator. A particle filtering approach is used for model adaptation and remaining useful performance 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.

Index Terms:Analog circuits, least squares–support vector machines, parametric faults, particle filters.

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