Prognostics for Aircraft Control Surface Damage Based on Fuzzy Least Squares Support Vector Regression (FLS-SVR)
The trends of flight control system state parameters which can be measured are indirect manifestations of surface damage. In order to predict the changes of states trend more accurately, an algorithm based on fuzzy least squares support vector regression (FLS-SVR) was presented. This approach reconstructed the phase space of multivariate time series using K-L transformation method. A FLS-SVR model was built with the new information priority theory according to apply a fuzzy membership to each input point. The SVR parameters were optimized by genetic algorithm (GA) to improve the accuracy of the model. In order to verify the validity of FLS-SVR algorithm, the prognostics and analysis of surface damage trend were performed. The simulation result demonstrates the prognostics model has good predictive ability.
KeywordsPrognostic support vector regression fuzzy membership least squares genetic algorithm
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