Bayesian Analysis of Reliability in Multicomponent Stress-Strength Models.

Abstract

This paper provides a Bayesian treatment of the problem of inference about the reliability of a multicomponent stress-strength system which functions if s or more of k identical components simultaneously operate. All stresses and strengths are assumed to be independent, exponentially distributed random variables. Exact and approximate asymptotic posterior distributions for the reliability are derived, and the various results are illustrated by numerical examples. Typically, components are mass produced and a sample of strengths can be generated from laboratory load tests on a random sample of the components. Also, the data of stress can be obtained from a simulation of conditions for operating the system. Thus, such data may be used for inference on the reliability of any s out of k system. It is not necessary to construct and test a complete system for each contemplated choice of s and k.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1976
Accession Number
ADA022746

Entities

People

  • Irwin Guttman
  • Norman Richard Draper

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Cooperation
  • Data Science
  • Information Science
  • Mathematics
  • Random Variables
  • Reliability
  • Simulations
  • Statistical Samples

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms