Identifying Key Factors in Turbofan Engine Health Degradation using Functional Analysis
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Abstract
A method is presented for predicting the health of turbofan engines using data and simulations from NASA. The method involves estimating engine health using k-nearest neighbors’ regression and fitting a remaining useful life model that considers engine usage. A matching pursuit algorithm identifies key parameters, while functional principal components provide insight into degradation precursors. Model performance is evaluated using root mean square error and future research and applications are discussed.
How to Cite
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Commercial Modular Aero-Propulsion System Simulation Turbofan Engine model, Functional Orthogonal Matching Pursuit, Functional Principal Component Analysis, Remaining Useful Life Prediction
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