The Internal States of the 3-Component Standby System.

Abstract

The study analyzes the internal states of a 3 component system with one active element and two spares in cold standby (pure replacement policy without repair). Elements of the system are assumed to have exponentially distributed lifetimes, however, special attention is paid to systems composed of components with different failure rates. The analysis is developed as a continuous time Markovian process with stationary transition probabilities. Probabilities that exactly i components have failed by time t are calculated based on three levels of information: for systems in unknown condition, for systems known to be in UP-condition and for systems whose condition was not observed for some amount of time. A key part is the investigation of conditional probabilities of i components having field by time t for a system known to be UP, the conditional limiting distribution as t approaches infinity, and relation to the system failure rate. State probabilities for systems into monitored continuously for being UP are shown to be bounded between those corresponding to systems that are either observed constantly or not at all. Keywords: Conditional State Probabilities; Limiting Distribution; Failure Rates as a Linear Function or Conditional State Probabilities.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1986
Accession Number
ADA175326

Entities

People

  • Karl-heinz E. Keitel

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Availability
  • California
  • Classification
  • Distribution Functions
  • Electrical Engineering
  • Engineering
  • Equations
  • Observation
  • Operations Research
  • Probability
  • Random Variables
  • Security
  • Stationary
  • Stochastic Processes
  • Survival
  • Technical Information Centers
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Fields of Study

  • Engineering

Readers

  • Logistics and Supply Chain Management.
  • Mathematical Modeling and Probability Theory.
  • Statistical inference.