Kuldip Rattan (Committee Member), Kefu Xue (Committee Member), Xiaodong Zhang (Advisor)
Master of Science in Engineering (MSEgr)
In this thesis, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation techniques. Engine sensor faults, actuator faults and component faults are considered under one unified framework. The fault diagnosis architecture consists of a bank of nonlinear adaptive estimators. One of them is the fault detection estimator used for fault detection, and the remaining ones are fault isolation estimators employed to determine the particular fault type/location after fault detection. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. The FDI architecture has been integrated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine model developed by NASA researchers in recent years. Extensive simulation results and comparative studies are conducted to verify the effectiveness of the nonlinear FDI method.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
Copyright 2012, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.