Development and Evaluation of a Bayesian Sequential Testing Methodology for Assessing the Reliability of Defense Systems.

Abstract

The concept of cost-effective testing of multi-component systems is central to this Project. The concept arose from a study of the testing of operational systems aimed at detecting the degradation of component reliabilities over time. This type of testing is usually conducted on a continual basis by system users to guard against the erosion of system reliability due to aging. Typically, the system, consisting of various subsystems or components, is brought in from the field for testing. The components are then tested in two ways: either independently in component-level tests, or simultaneously in system associated tests. In sum, there are a number of reasons why it is possible that all components in the system should not be tested the same or a proportionate number of times. The purpose of this project is to evaluate a Bayesian sequential testing methodology that considers these reasons and balances the cost of testing with the expected value of the information to be gained. The methodology indicates in sequential form which component or subsystem in the system to test next, and when to stop testing. This test plan developed should provide estimates of the reliability of complex systems more cost-effectively than the plans typically used.

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Document Details

Document Type
Technical Report
Publication Date
Mar 22, 1978
Accession Number
ADA136684

Tags

Communities of Interest

  • Counter WMD
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Bayesian Networks
  • Computer Programming
  • Computer Programs
  • Computers
  • Contracts
  • Department Of Defense
  • Failure Mode And Effect Analysis
  • Information Science
  • Military Research
  • Organizational Structure
  • Reliability
  • Risk
  • Test And Evaluation
  • Test Equipment
  • Test Facilities

Fields of Study

  • Engineering

Readers

  • Aerospace Test and Evaluation
  • Educational Psychology
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference