The subject of uncertainty in failure prognosis, including the importance of estimating and managing it, is a recurring topic in PHM literature. Considering that the prognosis task comprises forecasting, this could not be any different. However, prognosis performance metrics proposed in literature are usually concerned with measuring adherence to requirements, but not the adequate representation of the true uncertainty that arises from various sources in a prognosis problem. This paper presents statistically sound means for evaluating the performance of prognosis methods in the perspective of comparing the true uncertainty to its estimates. This provides a useful yet simple framework for failure prognosis performance evaluation.
How to Cite
performance metrics, Probability Integral Transform, failure prognosis, uncertainty
Berkowitz, J. (2001). Testing density forecasts, with applications to risk management. Journal of Business and Economic Statistics, 19(4), 465-474.
Celaya, J. R., Saxena, A., & Goebel, K. (2012). Uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation. In Proceedings of the international conference on prognostics and health management.
Chen, L., Lee, C., & Mehra, R. K. (2007). How to tell a bad filter through monte carlo simulations. IEEE Transactions on Automatic Control(52), 1302-1307.
Diebold, F. X., Gunther, T. A., & Tay, A. S. (1998). Evaluating density forecasts, with applications to financial risk management. International Economic Review, 39(4), 863-883.
Engel, S. J., Gilmartin, B. J., Bongort, K., & Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining. In Proceedings of IEEE aerospace conference.
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481.
Lea ̃o, B. P., Gomes, J. P. P., Galva ̃o, R. K. H., & Yoneyama, T. (2010). How to tell the good from the bad in failure prognostics methods. In Proceedings of IEEE aerospace conference.
Lea ̃o, B. P., & Yoneyama, T. (2011). Improvements on the offline performance evaluation of fault prognostics methods. In Proceedings of IEEE aerospace conference.
Lea ̃o, B. P., & Yoneyama, T. (2011). On the use of the un- scented transform for failure prognostics. In Proceedings of IEEE aerospace conference.
Lea ̃o, B. P., Yoneyama, T., Rocha, G. C., & Fitzgibon, K. T. (2008). Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit. In
Proceedings of the international conference on prognostics and health management.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Proceedings of the international con- ference on prognostics and health management.
Papoulis, A. (1991). Probability, random variables, and stochastic processes. McGraw-Hill.
Rosenblatt, M. (1952). Remarks on a multivariate transformation. The Annals of Mathematical Statistics, 23(3), 470-472.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management,
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems (1st ed.). Hoboken: John Wiley & Sons, Inc.
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.