Maintenance Decision-Making Under Prognostic and Diagnostic Uncertainty

Abstract

A key challenge faced by USAF maintenance personnel is the uncertainty associated with the information provided by diagnostic tools. This uncertainty results from accuracy issues associated with individual diagnostic tools, as well as inconsistencies across different diagnostic tools. This uncertainty can make it very difficult for maintenance technicians to choose an appropriate course of action. The end result is the possible omission of necessary maintenance actions and performance of unnecessary actions. B6th of these potential mistakes cause additional delays in returning an aircraft to the fleet and increased requirements for spare parts in the supply chain. Therefore, the objective of this project is to develop a methodology based on mathematical modeling that can be used to synthesize diagnostic information and provide a recommended course of action to a technician. This methodology potential could be incorporated into a decision-support tool for maintenance technicians. The activities required to achieve the objective of this project are applied to a hypothetical system. However, the definition of this hypothetical system is such that the system possesses fundamental characteristics like those systems utilized by the US Air Force (and many other organizations).

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA452058

Entities

People

  • Alejandro Mendoza
  • C. R. Cassady
  • Edward Pohl
  • Heather L. Nachtmann
  • Letitia Pohl
  • Nick Rew

Organizations

  • University of Arkansas

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Basic Programming Language
  • Engineering
  • Errors
  • Failure Mode And Effect Analysis
  • Governments
  • Logistics
  • Machine Learning
  • Maintenance
  • Maintenance Personnel
  • Neural Networks
  • Probability
  • Probability Distributions
  • Supply Chain
  • Technicians

Readers

  • Instructional Design and Training Evaluation.
  • Logistics and Supply Chain Management.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.