Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

Abstract : This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our 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.

[1]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[2]  Carl M. Harris,et al.  Fundamentals of queueing theory (2nd ed.). , 1985 .

[3]  J. Hannan,et al.  Introduction to probability and mathematical statistics , 1986 .

[4]  Keung-Chi Ng,et al.  Uncertainty management in expert systems , 1990, IEEE Expert.

[5]  N. Sheldon.  Introduction to Probability and Mathematical Statistics , 1991 .

[6]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[7]  John Haigh,et al.  Probabilistic Risk Analysis: Foundations and Methods , 2003 .

[8]  Fabrice Labeau,et al.  Discrete Time Signal Processing , 2004 .

[9]  Hugh McManus,et al.  A framework for understanding uncertainty and its mitigation and exploitation in complex systems , 2006, IEEE Engineering Management Review.

[10]  Jie Gu,et al.  Uncertainty Assessment of Prognostics of Electronics Subject to Random Vibration , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[11]  J. Wesley Hines,et al.  Uncertainty Management in Shock Models Applied to Prognostic Problems , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[12]  G. Kacprzynski,et al.  Advances in uncertainty representation and management for particle filtering applied to prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[13]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[14]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[15]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[16]  George Vachtsevanos,et al.  Methodologies for uncertainty management in prognostics , 2009, 2009 IEEE Aerospace conference.

[17]  Jonathan A. DeCastro,et al.  Exact Nonlinear Filtering and Prediction in Process Model-Based Prognostics , 2009 .

[18]  Sankaran Mahadevan,et al.  Uncertainty Quantification in Fatigue Crack Growth Prognosis , 2011 .

[19]  J. Celaya,et al.  Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies , 2011 .

[20]  Hong feng Wang.  Decision of Prognostics and Health management under Uncertainty , 2011 .

[21]  Gautam Biswas,et al.  A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging , 2011 .

[22]  Matthew Daigle,et al.  A Model-Based Prognostics Approach Applied to Pneumatic Valves , 2011 .

[23]  Kai Goebel,et al.  A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components , 2012, Infotech@Aerospace.

[24]  K. Goebel,et al.  Prognostics approach for power MOSFET under thermal-stress aging , 2012, 2012 Proceedings Annual Reliability and Maintainability Symposium.