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TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft
Alternative TitleTVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft
Hai Yang1; Wei Cheng1; Hong Zhu2
2008
Source PublicationCHINESE JOURNAL OF AERONAUTICS
ISSN1000-9361
Volume21Issue:5Pages:423-432
AbstractPredicting 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.
Other AbstractPredicting 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.
Keywordnon-stationary random vibration time-frequency distribution process neural network empirical mode decomposition
Indexed ByCSCD
Language英语
CSCD IDCSCD:3388996
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/145025
Collection中国科学院金属研究所
Affiliation1.Beijing Univ Aeronaut & Astronaut, Institute Solid Mech, Beijing 100191, Peoples R China
2.中国科学院金属研究所
Recommended Citation
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.
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