Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics

  • Marcos Orchard
  • Gregory Kacprzynski
  • Kai Goebel
  • Bhaskar Saha
  • George Vachtsevanos
Part of the Intelligent Systems, Control, and Automation: Science and Engineering book series (ISCA, volume 39)

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.


Particle Filter Correction Loop General Regression Neural Network Uncertainty Representation Remain Useful Life 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.zbMATHGoogle Scholar
  2. 2.
    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.Google Scholar
  3. 3.
    Specht, D.F., A general regression neural network, IEEE Transactions on Neural Networks 2(6), 568–576, November 1991.CrossRefGoogle Scholar
  4. 4.
    J.A. Leonard and M.A. Kramer, Radial basis function networks for classifying process faults, IEEE Control Systems 11, 31–38, 1991.CrossRefGoogle Scholar
  5. 5.
    T.A. Cruse, Probabilistic Systems Modeling and Validation, HCF 2004, March 16–18, 2004.Google Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    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.Google Scholar
  8. 8.
    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.Google Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    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.Google Scholar
  11. 11.
    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.Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  • Marcos Orchard
    • 1
    • 2
  • Gregory Kacprzynski
    • 3
  • Kai Goebel
    • 4
  • Bhaskar Saha
    • 4
  • George Vachtsevanos
    • 1
  1. 1.School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Electrical EngineeringUniversity of ChileSantiagoChile
  3. 3.Impact TechnologiesRochesterUSA
  4. 4.NASA Ames Research CenterMoffett FieldUSA

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