Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response due to Foreign Object Damage (FOD) Events
Author(s) -
James A. Turso,
Charles Lawrence,
Jonathan S. Litt
Publication year - 2006
Publication title -
journal of engineering for gas turbines and power
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.567
H-Index - 84
eISSN - 1528-8919
pISSN - 0742-4795
DOI - 10.1115/1.2718230
Subject(s) - turbofan , wavelet , noise (video) , computer science , event (particle physics) , kalman filter , accelerometer , engineering , artificial intelligence , pattern recognition (psychology) , automotive engineering , image (mathematics) , operating system , physics , quantum mechanics
: The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.
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