Chaotic behaviour identification of a rub-impact rotor using short-term predictability of measured data

Abstract

Abstract

Based on short-term predictability of a chaotic time series, this paper presents a new method for chaotic behaviour identification of a rub-impact rotor from measured data. The method is composed of two steps: in the first step, the phase space was reconstructed from measured data. Local prediction of the time series was performed in the second step with local fitting. Short-term predictability can be used to identify the chaotic property of the time series characterizing dynamic behaviour of the rub-impact rotor. The result of a local prediction is consistent with that of the correlation dimension and largest Lyapunov exponent estimated from measured data. The study shows that short-term predictability via local prediction can effectively identify chaos of a rub-impact rotor. The proposed method is simple, straightforward and suitable for real-time application.