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

IMR OpenIR

	TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft
其他题名	TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft
	Hai Yang 1; Wei Cheng 1; Hong Zhu 2
	2008
发表期刊	CHINESE JOURNAL OF AERONAUTICS
ISSN	1000-9361
卷号	21 期号:5 页码:423-432
摘要	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.
其他摘要	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.
关键词	non-stationary random vibration time-frequency distribution process neural network empirical mode decomposition
收录类别	CSCD
语种	英语
CSCD记录号	CSCD:3388996
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/145025
专题	中国科学院金属研究所
作者单位	1.Beijing Univ Aeronaut & Astronaut, Institute Solid Mech, Beijing 100191, Peoples R China 2.中国科学院金属研究所
推荐引用方式 GB/T 7714	Hai Yang,Wei Cheng,Hong Zhu. TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft[J]. CHINESE JOURNAL OF AERONAUTICS,2008,21(5):423-432.
APA	Hai Yang,Wei Cheng,&Hong Zhu.(2008).TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft.CHINESE JOURNAL OF AERONAUTICS,21(5),423-432.
MLA	Hai Yang,et al."TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft".CHINESE JOURNAL OF AERONAUTICS 21.5(2008):423-432.