Effects of Assuming Independent Component Failure Times, if They Actually Dependent, in a Series System.

Abstract

The overall objective of this proposal is to investigate the robustness to departures from independence of methods currently in use in reliability studies when competing failure modes or competing causes of failure associated with a single mode are present in a series system. The first specific aim is to examine the error one makes in modeling a series system by a model which assumes statistically independent component lifetimes when in fact the component lifetimes follow some multivariate distribution. The second specific aim is to assess the effects of the independence assumption error in estimating component parameters from life tests on series systems. In both cases, estimates of such errors will be determined via mathematical analysis and computer simulations for several prominent multivariate distributions. A graphical display of the errors for representative distributions will be made available to researchers who wish to assess the possible erroneous assumption of independent competing risks. A third aim is to tighten the bounds on estimates of component reliability when the risks belong to a general dependence class of distributions (for example, positive quadrant dependence, positive regression dependence, etc.). Major decisions involving reliability studies, based on competing risk methodology, have been made in the past and will continue to be made in the future. This study will provide the user of such techniques with a clearer understanding of the robustness of the analyses to departures from independent risks, an assumption commonly made by the methods currently in use.

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Document Details

Document Type
Technical Report
Publication Date
Oct 26, 1984
Accession Number
ADA150582

Entities

People

  • J. P. Klein
  • M. L. Moeschberger

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Applied Mathematics
  • Computational Science
  • Data Mining
  • Data Science
  • Failure Mode And Effect Analysis
  • Information Science
  • Life Tests
  • Plastic Explosives
  • Preventive Medicine
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Distributions
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Theoretical Analysis.