Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities
Abstract
A key challenge faced by U.S. Air Force maintenance personnel is the imperfection of aircraft diagnostic and prognostic capabilities. These imperfections include an inability to pinpoint failed components, the incorrect identification of failed components, and "cannot-duplicate" errors between the flight line and the depot. In addition to lowering morale in maintenance personnel, these imperfections lead to increased delays in returning aircraft mission-capable and excessive requirements for spare parts in the supply chain. Unfortunately, it is difficult to assess the impact of improvements to diagnostic and prognostic capabilities. The objective of this project is to develop a methodology based on mathematical modeling for analyzing these impacts. Specifically, the report addresses the following questions: (1) What impact do diagnostic and prognostic errors have on fleet performance?; (2) Given a specific investment in diagnostic improvements, what will the impact be in fleet performance?; and (3) Given a limited budget for diagnostic improvements, how should the funds be allocated to optimize fleet performance? The activities required to achieve the objective of this project are applied to hypothetical systems that possess several key characteristics similar to systems utilized by the U.S. Air Force. The authors create a set of mathematical and logical models that can be used to measure the performance of the fleet with and without diagnostic and prognostic errors. This permits the assessment of the impact of diagnostic and prognostic imperfections on fleet performance. Using these models, they explore the impact of specific investments in diagnostic and prognostic tools. This permits evaluation of the cost-effectiveness of potential diagnostic and prognostic improvement actions. They also incorporate the models into a decision-support environment that is designed to allocate investments in diagnostic improvements.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- ADA452091
Entities
People
- C. R. Cassady
- Edward Pohl
- Jason Honeycutt
- Letitia Pohl
- Mauricio Carrasco
Organizations
- University of Arkansas