TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive (TVAR) model based on the process neural network (PNN) and the empirical mode decomposition (EMD) is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions (IMFs). Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness,and feasibility, as well as higher frequency resolution.

作 者： Yang Hai Cheng Wei Zhu Hong   作者單位： Yang Hai(Institute of Solid Mechanics,Beijing University of Aeronautics and Astronautics,Beijing 100191,China;No.93033 Troop of People's Liberation Army,Shenyang 110030,China)

Cheng Wei(Institute of Solid Mechanics,Beijing University of Aeronautics and Astronautics,Beijing 100191,China)

Zhu Hong(Liaoning Equipment Manufacture College of Vocational Technology,Shenyang 110034,China) 

刊 名： 中國(guó)航空學(xué)報(bào)(英文版)  ISTIC 英文刊名： CHINESE JOURNAL OF AERONAUTICS  年，卷(期)： 2008 21(5)  分類號(hào)： V2  關(guān)鍵詞： non-stationary random vibration   time-frequency distribution   process neural network   empirical mode decomposition  

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