Reliability Prediction and Demonstration for Ground Electronic Equipment

Abstract

This study evaluates the accuracy of pre-design and stress analysis reliability prediction techniques, including the RADC Reliability Notebook, Volume II, stress analysis method, when applied to a variety of ground electronic equipment. Sources of prediction inaccuracy are investigated and identified. Program-related factors significant to the achievement of reliability and prediction accuracy are identified and a quantitive rating system is established and related to system prediction accuracy. New and improved pre-design and stress analysis reliability prediction methods are developed and tested. Equipment design approach categories having different prediction accuracy characteristics are identified with statistical distributions of prediction accuracy ratios. Degradation analysis processes and techniques are identified, evaluated, and presented with a recommended approach for their application. Reliability demonstration methods, including the Bayesian approach, are evaluated. A recommended reliability demonstration approach for ground electronic equipment is developed. A new base for integrated circuit failure tests is also provided.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1968
Accession Number
AD0844983

Entities

People

  • Dwight Q. Bellinger
  • Gerald M. Pittler
  • Robert E. Shelton

Organizations

  • TRW Inc.

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Acceptability
  • Circuit Boards
  • Correlation Analysis
  • Data Analysis
  • Data Mining
  • Data Science
  • Electron Tubes
  • Electronic Components
  • Electronic Equipment
  • Failure Mode And Effect Analysis
  • Information Processing
  • Information Science
  • Knowledge Management
  • Plastic Explosives
  • Printed Circuits
  • Statistical Distributions
  • Statistical Tests

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Software Engineering

Technology Areas

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