An uncertainty quantification framework for prognostics and condition-based monitoring

16th AIAA Non-Deterministic Approaches Conference

Volumn , Issue , 2014, Pages

  Sankararaman, Shankar ^a   Goebel, Kai ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NASA; STATE SPACE METHODS; SYSTEMS ENGINEERING; UNCERTAINTY ANALYSIS;

AMES RESEARCH CENTERS; BAYESIAN TRACKING; COMPUTATIONAL FRAMEWORK; CONDITION-BASED MONITORING; REMAINING USEFUL LIVES; SOURCES OF UNCERTAINTY; STATE - SPACE MODELS; UNCERTAINTY QUANTIFICATIONS;

CONDITION MONITORING;

EID: 85086494831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2514/6.2014-0480     Document Type: Conference Paper

References (17)

1. Jardine, A. K., Lin, D., and Banjevic, D., "A review on machinery diagnostics and prognostics implementing condition- based maintenance," Mechanical systems and signal processing, Vol. 20, No. 7, 2006, pp. 1483-1510.
2. Farrar, C. R. and Lieven, N. A., "Damage prognosis: the future of structural health monitoring," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 365, No. 1851, 2007, pp. 623-632.
3. Sankararaman, S., Ling, Y., Shantz, C., and Mahadevan, S., "Uncertainty quantification in fatigue damage prognosis," Annual conference of the prognostics and health management society, 2009.
4. Engel, S. J., Gilmartin, B. J., Bongort, K., and Hess, A., "Prognostics, the real issues involved with predicting life remaining," Aerospace Conference Proceedings, 2000 IEEE, Vol. 6, IEEE, 2000, pp. 457-469.
5. Saha, B. and Goebel, K., "Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques," Aerospace Conference, 2008 IEEE, IEEE, 2008, pp. 1-8.
6. Daigle, M., Saxena, A., and Goebel, K., "An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation," Annual Conference of the Prognostics and Health Management Society, 2012, pp. 326-335.
7. Sankararaman, S., Daigle, M., Saxena, A., and Goebel, K., "Analytical algorithms to quantify the uncertainty in remaining useful life prediction," Aerospace Conference, 2013 IEEE, IEEE, 2013, pp. 1-11.
8. Hastings, D. and McManus, H., "A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems," Engineering Systems Symposium MIT, Cambridge MA., 2004, p. 19.
9. Ng, K.-C. and Abramson, B., "Uncertainty Management in Expert Systems," IEEE Expert Systems, 1990, pp. 20.
10. Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., and Vachtsevanos, G., "Advances in uncertainty representation and management for particle filtering applied to prognostics," Prognostics and Health Management, 2008. PHM 2008. International Conference on, oct. 2008, pp. 1-6.
11. Tang, L., Kacprzynski, G., Goebel, K., and Vachtsevanos, G., "Methodologies for uncertainty management in prognostics," Aerospace conference, 2009 IEEE, march 2009, pp. 1-12.
12. Celaya, J. R., Saxena, A., and Goebel, K., "Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation," Proceedings of the Annual Conference of the PHM Society, 2012, pp. 23-27.
13. Sankararaman, S. and Mahadevan, S., "Model validation under epistemic uncertainty," Reliability Engineering & System Safety, Vol. 96, No. 9, 2011, pp. 1232-1241.
14. Sankararaman, S. and Goebel, K., "Why is the Remaining Useful Life Prediction Uncertain?" Annual Conference of the Prognostics and Health Management Society, 2013.
15. Haldar, A. and Mahadevan, S., Probability statistics and reliability in engineering design, John Wiley & Sons, 2000.
16. Najm, H. N., "Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics," Annual Review of Fluid Mechanics, Vol. 41, 2009, pp. 35-52.
17. Dwight, R. P. and Han, Z.-H., "Efficient uncertainty quantification using gradient-enhanced kriging," 11th AIAA Non- Deterministic Approaches Conference, 2009.