Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
N. Khiripet, G. Vachtsevanos, A. Thakker and T. Galie, A new confidence prediction neural network for machine failure prognosis, in Proceedings of Intelligent Ships Symposium IV, Philadelphia, PA, April 2–3, 2001.
Specht, D.F., A general regression neural network, IEEE Transactions on Neural Networks 2(6), 568–576, November 1991.
J.A. Leonard and M.A. Kramer, Radial basis function networks for classifying process faults, IEEE Control Systems 11, 31–38, 1991.
T.A. Cruse, Probabilistic Systems Modeling and Validation, HCF 2004, March 16–18, 2004.
J.L. Beck and S.K. Au, Bayesian updating of structural models and reliability using Marcov chain Monte Carlo simulation, Journal of Engineering Mechanics 128(4), 380–391, April, 2002.
M. Orchard, A particle filtering-based framework for on-line fault diagnosis and failure prognosis, Ph.D. Thesis, Department of Electrical and Computer Engineering, Georgia Institute of Technology, 2007.
C. Musso, N. Oudjane and F. Le Gland, Improving regularized particle filters, in Sequential Monte Carlo Methods in Practice, A. Doucet, N. De Frietas and N. Gordon (Eds.), Springer-Verlag, New York, 2001.
M. Orchard, B. Wu and G. Vachtsevanos, A particle filter framework for failure prognosis, in Proceedings of World Tribology Congress III, Washington DC, September 12–16, 2005.
R. Patrick, M. Orchard, B. Zhang, M. Koelemay, G. Kacprzynski, A. Ferri and G. Vacht-sevanos, An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis, in Proceedings of 42nd Annual Systems Readiness Technology Conference, AUTOTESTCON 2007, Baltimore, MD, September 2007.
R. Patrick-Aldaco, A model based framework for fault diagnosis and prognosis of dynamical systems with an application to helicopter transmissions, Ph.D. Thesis, Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2007.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science + Business Media B.V.
About this chapter
Cite this chapter
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., Vachtsevanos, G. (2009). Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics. In: Valavanis, K.P. (eds) Applications of Intelligent Control to Engineering Systems. Intelligent Systems, Control, and Automation: Science and Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3018-4_2
Download citation
DOI: https://doi.org/10.1007/978-90-481-3018-4_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3017-7
Online ISBN: 978-90-481-3018-4
eBook Packages: EngineeringEngineering (R0)