A Foreign Object Damage event detector Data Fusion System for turbofan engines

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 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 accelerometer data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model, providing both gas path and rotor dynamic structural response, which 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.