A wavelet-based approach for condition monitoring of a gas turbine during start-up transients
Résumé
When condition monitoring is performed during plant operational transients, the intrinsically dynamic behavior of the signals should be taken into account. To this aim, an approach based on i) the preprocessing of the signals by means of Haar wavelet transforms and ii) signal reconstruction by means of Auto-Associative Kernel Regression is here proposed. Its performance is verified with respect to a case study concerning the condition monitoring of a gas turbine during start-up transients. Copyright