Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems

Abstract

This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmental, and other stresses for long-term prediction. Furthermore, this work shows how these measures can be used to implement a novel feedback correction loop aimed to suggest modifications, at a system input level, with the purpose of extending the remaining useful life of a faulty nonlinear, non-Gaussian system. The correction scheme is tested and illustrated using real vibration feature data from a fatigue-driven fault in a critical aircraft component.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2010
Accession Number
ADA562626

Entities

People

  • Derek Edwards
  • George Vachtsevanos
  • Kai Goebel
  • Liang Tang
  • Marcos E. Orchard

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Aircraft Equipment
  • Aircrafts
  • Algorithms
  • Case Studies
  • Control Systems
  • Electrical Engineering
  • Engineering
  • Feedback
  • Filtration
  • Monte Carlo Method
  • Particles
  • Probability
  • Sequential Monte Carlo Methods
  • Signal Processing
  • Standards
  • Uncertainty
  • Unmanned Aerial Vehicles

Fields of Study

  • Engineering

Readers

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