Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

Electrolytic capacitors are used in several applications rang- ing from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past expe- riences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress condi- tions during operations. This makes them good candidates for prognostics and health management. Model-based prognos- tics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation mod- els for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.