A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

By: Administration (Nasa), National Aeronaut

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data. Turso, James A. and Litt, Jonathan S. Glenn Research Center NASA/TM-2004-213192, ARL-TR-3201, AIAA Paper 2004-4047, E-14691 DA Proj. 1L1-61102-AF-20; WBS 22-303-30-72 TURBOFAN ENGINES; DETECTORS; MULTISENSOR FUSION; FOREIGN BODIES; IMPACT DAMAGE; PROBABILITY THEORY; GAS PATH ANALYSIS; KALMAN FILTERS; WAVELET ANALYSIS; FUZZY SETS; ACCELEROMETERS; DYNAMIC RESPONSE

• Format: Paperback
• Language: English
• ISBN: B08DSX38JP
• ISBN13: 9798669138738
• Release Date: January 1
• Publisher: Independently Published
• Length: 28 Pages
• Weight: 0.20 lbs.
• Dimensions: 0.1" x 8.5"