A new arithmetic for aeroengine forecasting based on Chaos

Firstly, the noise was removed from the original exhaust gas temperature(EGT) data series using haar and db16 wavelets. Further, though analysing the feature of the denoised data series, It indicated that there existed a chaos feature in it. Then, chaotic forecasting arithmetic was established by using chaos theory, and the EGT data series can be forecasted by the arithmetic. Finally, the condition of aeroengine was defined by comparing the series with the red line and testing the stable level of data series. The proposed arithmetic was verified through some type of aircraft aeroengine EGT data series obtained from actual flight. The result shows that the proposed arithmetic has a better forecast accuracy than adding-weight one-rank local-region arithmetic and Auto-Regressive and Moving Average(ARMA) arithmetic. It can be used as a supporting model in the decision making of this aeroengine fault forecast.