Optimal Periodic Inspection of A Stochastically Degrading System

Abstract

This thesis develops and analyzes a procedure to determine the optimal inspection interval that maximizes the limiting average availability of a stochastically degrading component operating in a randomly evolving environment. The component is inspected periodically, and if the total observed cumulative degradation exceeds a fixed threshold value, the component is instantly replaced with a new, statistically identical component. Degradation is due to a combination of continuous wear caused by the component's random operating environment, as well as damage due to randomly occurring shocks of random magnitude. In order to compute an optimal inspection interval and corresponding limiting average availability, a nonlinear program is formulated and solved using a direct search algorithm in conjunction with numerical Laplace transform inversion. Techniques are developed to significantly decrease the time required to compute the approximate optimal solutions. The mathematical programming formulation and solution techniques are illustrated through a series of increasingly complex example problems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA445188

Entities

People

  • Timothy B. Booher

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Computational Complexity
  • Computational Science
  • Computer Programming
  • Differential Equations
  • Failure Mode And Effect Analysis
  • Maintenance
  • Markov Processes
  • Mathematical Models
  • Mathematical Programming
  • Operations Research
  • Probability Distributions
  • Random Variables
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Control Systems Engineering.
  • Materials Science and Engineering.
  • Operations Research