Date of Award

6-18-2015

Document Type

Thesis

Degree Name

Master of Science in Systems Engineering

Department

Department of Systems Engineering and Management

First Advisor

Jason Freels, PhD.

Abstract

The Air Force current operations continue to undergo significant changes compelled by decreasing fiscal appropriations, aging aircraft, and personnel drawdown. The Air Force must effectively improve current maintenance operations in part to deal with these challenges. This study will explore the area of the A-10 aircraft fleet's TF34-100 high-pass turbo-fan engine sensor data to seek its deterioration modelling and prognostics capability. In futurity this will allow for achievement of greater confidence in predicting the compressor stall which leads to engine performance deterioration and a costly repair in maintenance. By utilizing an innovative method to forecast the probability of compressor stall, according to individual engine sensor data which has recently become available, it will be possible to achieve significant benefits in both maintenance planning and mission scheduling (which will greatly reduce the associated costs of maintenance servicing).

AFIT Designator

AFIT-ENV-MS-15-J-036

DTIC Accession Number

ADA621762

Comments

Co-authored thesis.

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