A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors

Avionics systems play a critical role in many aspects of aircraft flight control. As the complexity of these systems increase, the chances of in-flight malfunctions are also likely to increase. This drives the need for Integrated Vehicle Health Management (IVHM) technologies for flight-critical avionics. Studying and analyzing the performance degradation of embedded electronics in the aircraft domain will help to increase aircraft reliability, assure in-flight performance, and reduce maintenance costs. Further, an understanding of how components degrade as well as the capability to anticipate failures and predict the remaining useful life (RUL) can provide a framework for condition-based maintenance. To support a condition-based maintenance and a safety-critical analysis framework, this thesis conducts a detailed study of the degradation mechanisms of electrolytic capacitors, an important component of most electronic systems.;Electrolytic capacitors are known to have lower reliability than other electronic components that are used in power supplies of avionics equipment and electrical drivers of electro-mechanical actuators of control surfaces. Therefore, condition-based health assessment that leverages the knowledge of the device physics to model the degradation process can provide a generalized approach to predict remaining useful life as a function of current state of health and anticipated future operational and environmental conditions.;We adopt a combined model and data-driven (experimental studies) approach to develop physics-based degradation modeling schemes for electrolytic capacitors. This approach provides a framework for tracking degradation and developing dynamic models to estimate the RUL of capacitors. The prognostics and RUL methodologies are based on a Bayesian tracking framework using the Kalman filter and Unscented Kalman filter approaches.;The thesis makes contributions to physics-based modeling and a model-based prognostics methodology for electrolytic capacitors. Results discuss prognostics performance metrics like the median relative accuracy and the α-λ (alpha-lambda) accuracy. We have also demonstrated the derived physics-based degradation model is general, and applied to both accelerated and nominal degradation phenomena. Our overall results are accurate and robust, and, therefore, they can form the basis for condition-based maintenance and performance-based evaluation of complex systems.