| 航天器非平稳振动环境TVAR时频分析(英文) |
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| 引用本文： | Yang Hai a,b,Cheng Wei a,Zhu Hong c a Institute of Solid Mechanics,Beijing University of Aeronautics and Astronautics,Beijing ,China bNo. Troop of People’ s Liberation Army,Shenyang ,China cLiaoning Equipment Manufacture College of Vocational Technology,Shenyang ,China.航天器非平稳振动环境TVAR时频分析(英文)[J].中国航空学报,2008,21(5):423-432. |
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| 作者姓名： | Yang Hai a b Cheng Wei a Zhu Hong c a Institute of Solid Mechanics Beijing University of Aeronautics and Astronautics Beijing China bNo. Troop of People’ s Liberation Army Shenyang China cLiaoning Equipment Manufacture College of Vocational Technology Shenyang China |
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| 作者单位： | [1]Institute of Solid Mechanics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China [2]No.93033 Troop of People＇s Liberation Army; Shenyang 110030, China [3]Liaoning Equipment Manufacture College of Vocational Technology, Shenyang 110034, China |
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| 基金项目： | Foundation item： Aeronautical Science Foundalion of China （20071551016） |
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| 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.
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| 关 键 词： | 不固定性随性振动 时间频率分布 程序神经网络 太空船 |
| 收稿时间： | 10 March 2008 |
TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft |
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| Yang Hai,Cheng Wei,Zhu Hong.TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft[J].Chinese Journal of Aeronautics,2008,21(5):423-432. |
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| Authors: | Yang Hai Cheng Wei Zhu Hong |
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| Institution: | [1]Institute of Solid Mechanics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China; [2]No.93033 Troop of People＇s Liberation Army; Shenyang 110030, China; [3]Liaoning Equipment Manufacture College of Vocational Technology, Shenyang 110034, China |
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| Abstract: | 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 su-perposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution. |
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| Keywords: | non-stationary random vibration time-frequency distribution process neural network empirical mode decomposition |
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