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Prognostics Center of Excellence Wins Best Paper Awards

Two papers written by Prognostics Center of Excellence researchers won best paper awards at the Annual Conference of the Prognostics & Health Management Society 2012, held at Minneapolis, MN, September 23-27. The paper “Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms Based on Kalman Filter Estimation,” by José Celaya, Kai Goebel, and Abhinav Saxena, won the best paper award for theoretical paper; and the paper “Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor Under Thermal Overstress Conditions,” by Gautam Biswas, José Celaya, Kai Goebel, and Chetan Kulkarni, won the best paper award for application paper.

BACKGROUND: Within the System-wide Safety and Assurance Technologies (SSAT) project, health management techniques, and specifically, fault prognostics techniques, are explored for components, subsystems, and systems in aeronautics. “Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms Based on Kalman Filter Estimation” discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful-life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful-life probability density function and the true remaining useful-life probability density function is explained, and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

“Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor Under Thermal Overstress Conditions” presents the latest research results in prognostics for electrolytic capacitors. Electrolytic capacitors are used in several applications, ranging from power supplies for safety-critical avionics equipment to power drivers for electro-mechanical actuators. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics 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 models 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. The overall goal is to derive accurate models of capacitor degradation, and use them to predict remaining useful life in DC-DC converters.

PROGRAM FUNDING: System-wide Safety and Assurance Technologies (SSAT) project, Aviation Safety Program, Aeronautics Research Mission Directorate (ARMD); and the Office of the Chief Technologist (OCT)/Advanced Exploration Systems (AES) project, Advanced Cryogenic Loading Operations (ACLO)

TEAM: José R. Celaya (SGT), Kai Goebel (NASA), and Abhinav Saxena (SGT)

COLLABORATORS: Gautam Biswas (Vanderbilt University) and Chetan Kulkarni (Vanderbilt University)

Contact: José R. Celaya

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