Efficient Sensitivity Methods for Probabilistic Lifing and Engine Prognostics

Abstract

Probabilistic engine health management (PHM) is expected to be a go-forward approach for the USAF and other DoD agencies to enable dramatic improvements in the assessment and management of military assets. As a result, accurate and information-rich probabilistic lifing methods are essential to assess the benefits of technology insertion programs for PHM. As such, under this program three technology thrusts were investigated: a) sensitivity methods probability-of-failure estimates with respect to POD curve parameters, b) complex variable methods for sensitivity analysis, and c) probabilistic sensitivity analysis with respect to bounds of truncated distributions.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2010
Accession Number
ADA533813

Entities

People

  • Andrew Bates
  • Andy Voorhees
  • Dominique N. Wagner
  • Harry Millwater
  • Jose M. Garza
  • Ronald Bagley

Organizations

  • University of Texas at San Antonio

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Complex Variables
  • Computational Fluid Dynamics
  • Computational Science
  • Differential Equations
  • Equations
  • Finite Element Analysis
  • Geometry
  • Integrals
  • J Integrals
  • Mechanics
  • Probability
  • Probability Density Functions
  • Random Variables
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerospace Engineering
  • Statistical inference.