A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Mar 26, 2021
Marcos E. Orchard Liang Tang Kai Goebel George Vachtsevanos

Abstract

Particle filters (PF) have been established as the de facto state of the art in failure prognosis, and particularly in the representation and management of uncertainty in long-term predictions when used in combination with outer feedback correction loops. This paper presents a novel Risk- Sensitive PF (RSPF) framework that complements the benefits of the classic approach, by representing the probability of rare and costly events within the formulation of the nonlinear dynamic equation that describes the evolution of the fault condition in time. The performance of this approach is thoroughly compared using a set of proposed metrics for prognosis results. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

How to Cite

E. Orchard, M., Tang, L., Goebel, K., & Vachtsevanos, G. (2021). A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1587
Abstract 7 | PDF Downloads 2

##plugins.themes.bootstrap3.article.details##

Keywords

particle filtering, prognostics, risk assessment, uncertainty management

References
(Andrieu, 2001) C. Andrieu, A. Doucet, and E. Punskaya, “Sequential Monte Carlo Methods for Optimal Filtering,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag, 2001.
(Arulampalam , 2002) M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, Feb. 2002, pp. 174 – 188.
(Cruse, 2004) T.A. Cruse, “Probabilistic Systems Modeling and Validation,” HCF 2004, March 16- 18, 2004.
(Doucet, 1998) A. Doucet, “On sequential Monte Carlo methods for Bayesian Filtering,” Technical Report, Engineering Department, Univ. Cambridge, UK, 1998.
(Doucet, 2001) A. Doucet, N. de Freitas, and N. Gordon, “An introduction to Sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag, 2001.
(Khiripet, 2001) N. Khiripet, G. Vachtsevanos, A. Thakker, and T. Galie, “A New Confidence Prediction Neural Network for Machine Failure Prognosis”, Proceedings of Intelligent Ships Symposium IV, Philadelphia, PA, April 2-3, 2001.
(Orchard, 2005) M. Orchard, B. Wu, and G. Vachtsevanos, “A Particle Filter Framework for Failure Prognosis”, Proceedings of World Tribology Congress III, Washington DC, Sept. 12-16, 2005.
(Orchard, 2008) M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, and G. Vachtsevanos, “Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics,” 2008 International Conference on Prognostics and Health Management PHM 2008, Denver, CO, USA, October 9 – 12, 2008.
(Orchard, 2009) M. Orchard, On-line Fault Diagnosis and Failure Prognosis Using Particle Filters. Theoretical Framework and Case Studies, Publisher: VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG, Saarbrücken, Germany, April 2009, 108 pages. Atlanta: The Georgia Institute of Technology, Diss., 2007.
(Patrick, 2007) R. Patrick, M. Orchard, B. Zhang, M. Koelemay, G. Kacprzynski, A. Ferri, and G. Vachtsevanos, “An Integrated Approach to Helicopter Planetary Gear Fault Diagnosis and Failure Prognosis,” 42nd Annual Systems Readiness Technology Conference, AUTOTESTCON 2007, Baltimore, USA, September 2007.
(Shafer, 1976) G. Shafer, A mathematical theory of evidence, Princeton, N.J: Princeton University Press, 1976.
(Specht, 1991) D.F. Specht, “A General Regression Neural Network”, IEEE Trans on Neural Networks, vol. 2, no. 6, pp. 568-76, Nov, 1991.
(Thrun, 2001) S. Thrun, J. Langford, and V. Verma, “Risk Sensitive Particle Filters,” Neural Information Processing Systems (NIPS), Dec. 2001.
(Verma, 2004) V. Verma, G. Gordon, R. Simmons, and S. Thrun, “Particle Filters for Rover Fault Diagnosis,” IEEE Robotics & Automation Magazine, pp. 56 – 64, June 2004.
(Vachtsevanos, 2006) G. Vachtsevanos, F.L. Lewis, M.J. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Hoboken, NJ, John Wiley and Sons, 2006.
(Zhang, 2009) B. Zhang, T. Khawaja, R. Patrick, M. Orchard, A. Saxena, and G. Vachtsevanos, “A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 2, pp. 303-310, February 2009.
Section
Technical Papers

Most read articles by the same author(s)

1 2 3 4 5 6 > >>