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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.

Keywords

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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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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