Bayesian Component Reliability Estimation: an F-35 Case Study

Abstract

A challenging aspect of a system reliability assessment is integrating multiple sources of information, such as component, subsystem, and full-system data, along with previous test data or subject matter expert (SME) opinion. A powerful feature of Bayesian analyses is the ability to combine these multiple sources of data and variability in an informed way to perform statistical inference. This feature is particularly valuable in assessing system reliability where testing is limited and only a small number of failures (or none at all) are observed. The F-35 is DoD's largest program; approximately one-third of the operations and sustainment cost is attributed to the cost of spare parts and the removal, replacement, and repair of components. The failure rate of those components is the driving parameter for a significant portion of the sustainment cost, and yet for many of these components, available estimates of the failure rate are poor. For many programs, the contractor produces estimates of component failure rates based on engineering analysis and legacy systems with similar parts. While these estimates are useful, the actual removal rates provide a more accurate estimate of the removal and replacement rates the program will experience in future years. In this document, we show how we applied a Bayesian analysis to combine the engineering reliability estimates with the actual failure data to estimate component reliability. Our analysis technique also allows for us to overcome the problems of cases where few or no failures have been observed. We are able to show that combining the engineering knowledge of reliability with the observed operational reliability results in both a more informed estimate of each individual component's reliability and a more informed estimate of overall F-35 maintenance costs.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2019
Accession Number
AD1122379

Entities

People

  • Rebecca M. Medlin
  • Vincent A. Lillard

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Bayesian Networks
  • Case Studies
  • Contractors
  • Contracts
  • Data Analysis
  • Data Science
  • Department Of Defense
  • Engineering
  • Fighter Aircraft
  • Governments
  • Information Science
  • Maintenance
  • Maintenance Costs
  • Spare Parts
  • Statistical Inference
  • Statistics
  • Sustainment
  • Test And Evaluation
  • Training
  • Vehicles
  • Virginia

Fields of Study

  • Engineering

Readers

  • Inertial Navigation Systems.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference