Date of Award
Master of Science in Systems Engineering
Department of Systems Engineering and Management
Jason Freels, PhD.
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).
DTIC Accession Number
Li, Shuxiang A. and Jones, Trevor G., "A Method to Predict Compressor Stall in the TF34-100 Turbofan Engine Utilizing Real-Time Performance Data" (2015). Theses and Dissertations. 197.