Aeroengine Prognostics via Local Linear Smoothing, Filtering and Prediction

We propose a new method for local linear smoothing, filtering and prediction of noisy data. Its novelty consists in two of its steps: a sliding window filter that uses Student’s t-statistics to perform smoothing and filtering, and a trend change detection scheme that uses a convex hull construction to determine a change of slope or intercept of the local linear trend. The final linear trend detected is used for linear prediction and interval estimation. The application of the scheme to gas-turbine engine prognostics is presented.