Analytical Results for a Single-Unit System Subject to Markovian Wear and Shocks

Abstract

This thesis develops and analyzers a mathematical model for the reliability measures of a single-unit system subject to continuous wear due to its operating environment and randomly occurring shocks that inflict a random amount of damage to the unit. Assuming a Markovian operating environment and shock arrival mechanism, Laplace-Stieltjes transform expressions are obtained for the failure time distribution and all of its moments. Moreover, an analytical expression is derived for the long-run availability of the single-unit system when it is subject to an inspect-and-replace maintenance policy. The analytical results are illustrated, and their results compared with those of Monte Carlo-simulated failure data. The numerical results indicate that the reliability measures may be accurately computed via numerical inversion of the transform expressions in a straightforward manner when the input parameters are known a priori. In stark contrast to the simulation model which requires several hours to obtain the reliability measures, the analytical procedure computes the same measures in only a few seconds.

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 2004
Accession Number
ADA422939

Entities

People

  • Daniel E. Finkelstein

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Computational Science
  • Computer Simulations
  • Differential Equations
  • Failure Mode And Effect Analysis
  • Markov Processes
  • Mathematical Models
  • Partial Differential Equations
  • Probabilistic Models
  • Probability Distributions
  • Random Variables
  • Reliability
  • Second World War
  • Simulations
  • Stochastic Processes
  • United States

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Statistical inference.