The Conceptual Design Reliability Prediction Method: Establishing Functional-Physical Reliability Relationships for System Reliability Predictions During Conceptual Design

Abstract

This paper presents a system reliability prediction method suitable for use during conceptual design, called the conceptual design reliability prediction method (CDRPM). The CDRPM extends the early design reliability prediction method (EDRPM) by facilitating parameter characterizations that follow non-normal distributions. Functional-physical reliability relationships are established through a hierarchical Bayesian model solved by Markov Chain Monte Carlo (MCMC) sampling and aggregated using the systems reliability block diagram (RBD) to assess the likelihood of candidate architectures meeting a reliability requirement. Reliability predictions based on different types of failure data, specifically success ratios and failure rates, are compared herein in a case study of a generic launcher system assessed by the CDRPM. This research shows the effects of failure data type selection and distribution assumptions on architecture down-selection, leading to enhanced insight during conceptual design analysis.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2020
Accession Number
AD1126560

Entities

People

  • Elizabeth T. Rajchel

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Business Administration
  • Complex Systems
  • Computational Science
  • Computer Programming
  • Databases
  • Engineers
  • Failure Mode And Effect Analysis
  • Information Science
  • Mechanical Engineering
  • Monte Carlo Method
  • Operations Research
  • Probability
  • Reliability
  • Risk Analysis
  • Systems Engineering
  • Systems Management
  • Test And Evaluation
  • Transport Aircraft

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Software Engineering
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference