Decision Analysis Applied to Inspection Interval Decisions

Abstract

Using decision analysis techniques, a general model was developed for base-level aircraft inspection interval decisions. This model differs from current methods such as actuarial analysis and the Computer Monitored Inspection Program in that it is designed to define and measure the significance of the subjective uncertainties and risks inherent in inspection interval decisions. Failure data, cost data, expert opinion, and decision maker preferences are brought together in a single, unified decision making framework. Decision alternatives are evaluated based on the entire cost picture (i.e. repair cost, opportunity costs, and inspection costs). The general model developed in this thesis can serve as a starting point for the analysis, but it must be tailored for the actual subsystems to which it is applied. Once the specific model for a given subsystem is built, it can be analyzed using existing software packages. An example of how the tailoring and analysis may be accomplished is provided in a detailed study of the B-1B Anti-Skid subsystem. The decision analysis approach will be most advantageous when used on subsystems which have potentially serious failure consequences or economical concerns. Keywords: Aircraft maintenance; Decision theory; Intervals; Preventive maintenance; Theses.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1988
Accession Number
ADA202933

Entities

People

  • Kermit L. Stearns Ii

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Aircraft Equipment
  • Aircraft Maintenance
  • Aircrafts
  • Computer Science
  • Computers
  • Databases
  • Delphi Method
  • Maintenance
  • Maintenance Personnel
  • Operations Research
  • Preventive Maintenance
  • Probability Distributions
  • Random Variables
  • Statistical Analysis
  • Test And Evaluation

Fields of Study

  • Engineering

Readers

  • Logistics and Supply Chain Management.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy