Evaluating Failure Time Probabilities for a Markovian Wear Process

Abstract

We present simplified analytical results for the numerical evaluation of failure time probabilities for a single-unit system whose cumulative wear over time depends on its external environment. The failure time distribution is derived as a one-dimensional Laplace-Stieltjes transform with respect to the temporal variable using a direct solution approach and by inverting an existing two-dimensional result with respect to the spatial failure threshold variable. Two numerical examples demonstrate that accurate cumulative probability values can be obtained in a straightforward manner using standard computing environments. Reliability models that incorporate the effect of a stochastic and dynamic environment on a unit's lifetime have attracted a moderate amount of attention in the past decade. However, evaluating failure time probabilities using such models is nontrivial in all but a few cases. Kharoufeh [1] provided a closed-form lifetime distribution for a continuous Markovian wear process as a two-dimensional Laplace transform. The main purpose of this paper is to reduce the lifetime distribution to a one-dimensional Laplace transform in order to facilitate simpler numerical implementation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA596314

Entities

People

  • Jeffrey A. Sipe
  • Jeffrey P. Kharoufeh

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • C Programming Language
  • Computer Programming
  • Computers
  • Cutting Tools
  • Distribution Functions
  • Markov Processes
  • Mathematical Models
  • Monte Carlo Method
  • Operations Research
  • Probabilistic Models
  • Probability
  • Random Variables
  • Reliability
  • Stochastic Processes
  • Test And Evaluation
  • Two Dimensional

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design