This article discusses several aspects of uncertainty representation and management for model-based prognostics method- ologies 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.
How to Cite
Bedford, T., & Cooke, R. M. (2001). Probabilistic risk analysis: Foundations and methods. Cambridge University Press.
Celaya, J., Kulkarni, C., Biswas, G., & Goebel, K. (2011, September 25-29). A model-based prognostics methodology for electrolytic capacitors based on electrical overstress accelerated aging. In Proceedings of annual conference of the phm society. Montreal, Canada.
Celaya, J., Kulkarni, C., Saha, S., Biswas, G., & Goebel, K. (2012, January). Accelerated aging in electrolytic capacitors for prognostics. In 2012 proceedings - annual reliability and maintainability symposium (rams) (p. 1 -6). doi: 10.1109/RAMS.2012.6175486
Celaya, J., Saxena, A., & Goebel, K. (2012, June 19-21). A discussion on uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation applied to prognostics of electronics components. In Infotech@aerospace 2012 online conference proceedings. Garden Grove, California.
Celaya, J., Saxena, A., Kulkarni, C., Saha, S., & Goebel, K. (2012, January). Prognostics approach for power mosfet under thermal-stress aging. In 2012 proceedings - annual reliability and maintainability symposium (rams) (p. 1 -6). doi: 10.1109/RAMS.2012.6175487
Celaya, J. R., Saxena, A., Saha, S., & Goebel, K. (2011, September 25-29). Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In Proceedings of annual conference of the phm society. Montreal, Canada.
Daigle, M. J., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2 (2)(008).
DeCastro, J. A. (2009). Exact nonlinear filtering and pre-diction in process model-based prognostics. In Annual conference of the prognostics and health management society. San Diego, CA..
deNeufville, R. (2004). Uncertainty management for engineering systems planning and design. In Engineering systems symposium mit. Cambridge, MA..
Engel, S. J. (2009). PHM engineering perspectives challenges and ‘crossing the valley of death’. In Annual conference of the prognostics and health management society. San Diego, CA..
Gross, D., & Harris, C. M. (1998). Fundamentals of queueing theory (3rd ed.). John Wiley & Sons Inc.
Gu, J., Barker, D., & Pecht, M. (2007). Uncertainty assessment of prognostics of electronics subject to random vibration. In Artificial intelligence for prognostics: Pa- pers from the aaai fall symposium.
Hastings, D., & McManus, H. (2004). A framework for understanding uncertainty and its mitigation and exploitation in complex systems. In Engineering systems symposium mit (p. 19). Cambridge MA..
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME– Journal of Basic Engineering, 82 (Series D), 35-45.
Ng, K.-C., & Abramson, B. (1990). Uncertainty management in expert systems. IEEE Expert Systems, 20.
Oppenheim, A. V., & Schafer, R. W. (1989). Discrete-time signal processing. Prentice Hall.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008, oct.). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Prognostics and health management, 2008. phm 2008. international conference on (p. 1 -6). doi: 10.1109/PHM.2008.4711433
Saha, B., & Goebel, K. (2008, march). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. In Aerospace conference, 2008 ieee (p. 1 -8). doi: 10.1109/AERO.2008.4526631
Saha, B., & Goebel, K. (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. In Proceedings of annual conference of the phm society.
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009, feb.). Prognostics methods for battery health monitoring using a bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291 -296. doi: 10.1109/TIM.2008.2005965
Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2011). Uncertainty quantification in fatigue crack growth prognosis. International Journal of Prognostics and Health Management, 2-1(1).
Saxena, A., Celaya, J. R., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1 (1)(001).
Tang, L., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2009, march). Methodologies for uncertainty management in prognostics. In Aerospace conference, 2009 ieee (p. 1 -12). doi: 10.1109/AERO.2009.4839668
Usynin, A., & Hines, J. W. (2007). Uncertainty management in shock models applied to prognostic problems. In Artificial intelligence for prognostics: Papers from the aaai fall symposium.
Wang, H.-f. (2011, January). Decision of prognostics and health management under uncertainty. International Journal of Computer Applications, 13(4), 1–5. (Published by Foundation of Computer Science)
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.