Markov Modeling of Component Fault Growth Over a Derived Domain of Feasible Output Control Effort Modifications

Abstract

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system's components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
ADA588724

Entities

People

  • Brian Bole
  • George Vachtsevanos
  • Kai Goebel

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Control Systems
  • Dynamics
  • Energy Management
  • Engineering
  • Markov Models
  • Markov Processes
  • Notation
  • Optimization
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Stochastic Processes
  • Supply Chain Management
  • United States

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.
  • Speech Processing/Speech Recognition.

Technology Areas

  • Space