An effective prediction model for aircraft chaos time series based on echo state networks(ESN)

It is significant for flight safety to accurately detect the coming fault of aircraft or predict its change trend. Aiming at suppressing the shortcoming of fault prediction based on traditional ESN, we present a new prediction method combining ESN with wavelet denoising. Sections 1 and 2 of the full paper explain our prediction model mentioned in the title, which we believe is effective and whose core is: the method not only reserves the advantages of ESN model in nonlinear time series prediction but also reduces the noise influence in practice, i.e., the pretreatment via wavelet transform will be done before prediction. Section 3 concerns a certain type of aero-engine lubricator. Its simulation results are presented in Figs. 4, 5, 7, 8 and Tables 1 and 2. The simulation results and their analysis show preliminarily that the proposed method improves the prediction accuracy of nonlinear chaotic time series including noises, thus indicating that the proposed model is an effective approach in actual application.