On Failure Modeling,

Abstract

A promising approach to failure modeling, in particular to developing failure-time distributions, is discussed. Under this approach, system state or wear and tear is modeled by an approximately chosen random process, eg, a diffusion process; and the occurrences of fatal shocks are modeled by a Poisson process whose rate function is state dependent. The system is said to fail when either wear and tear accumulates beyond in acceptable or safe level or a fatal shock occurs. This approach has significant merit. First it provides revealing new insights into most of the famous and frequently used lifetime distributions in reliability theory. Moreover, it suggests intuitively appealing ways for enhancing those standard models. Indeed, this approach provides a means of representing the underlying dynamics inherent in failure processes. Reasonable postulates for the dynamics of failure should lend credence to prediction and estimation of reliability, maintainability, and availability. In other words, accuracy of representation could lead to better, more reliable prediction of failure. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1984
Accession Number
ADA141760

Entities

People

  • A. J. Lemoine
  • M. L. Wenocur

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Brownian Motion
  • Data Analysis
  • Difference Equations
  • Differential Equations
  • Equations
  • Gaussian Distributions
  • Markov Processes
  • Mathematical Models
  • Models
  • Partial Differential Equations
  • Probability
  • Random Variables
  • Reliability
  • Shot Noise
  • Standards
  • Statistical Analysis
  • Stochastic Processes

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