Application of Bayesian Reliability Concepts to Cruise Missile Electronic Components

Abstract

The purpose of this thesis was to evaluate the applicability of Bayesian statistical methods to the problem of determining cruise missile component reliability. There were three objectives: 1) to develop models incorporating Bayesian reliability concepts that can be used to predict component reliability based on data available in a program transitioning from development to production; 2) to determine the model's validity in comparison with classical statistical models; and 3) to assess the accuracy of both approaches against actual cruise missile flight test history. A total of six models were developed for the failure rate of the Tomahawk Cruise Missile Guidance Set using both exponential and binomial distributions. The flight test data seemed to belong to another failure distribution, and was not useful as a measure of performance as had been proposed. The Bayesian Expert Information Model provided reasonable point estimates of the failure rate and markedly shorter 90% confidence intervals. In general, the Bayesian models had confidence intervals that were shorter than the classical statistical inference models, allowing a more accurate decision-making process.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1989
Accession Number
ADA216208

Entities

People

  • Richard K. Lemaster

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Bayesian Inference
  • Bayesian Networks
  • Confidence Limits
  • Data Mining
  • Data Science
  • Databases
  • Failure Mode And Effect Analysis
  • Goodness Of Fit Tests
  • Information Science
  • Knowledge Management
  • Probability
  • Probability Distributions
  • Statistical Algorithms
  • Statistical Inference
  • Test And Evaluation

Readers

  • Computational Modeling and Simulation
  • Inertial Navigation Systems.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics