基于时频分析的运载火箭故障特征提取技术

崔桂磊; 唐永华

基于时频分析的运载火箭故障特征提取技术

宇航动力学国家重点实验室, 西安 710043

基金项目: 总装预研项目(2009SY2603009)

详细信息

中图分类号: V557⁺.3;TJ760.6⁺28;TN911.72
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- 被引次数: 0
出版历程
- 收稿日期: 2011-03-10
- 网络出版日期: 2012-06-30

Rocket fault feature extraction based on time-frequency analysis

State Key Laboratory of Astronautic Dynamics, Xian 710043, China

摘要

摘要: 为尽可能从运载火箭飞行试验数据中获取更多有价值的信息,提出了新的火箭振动参数故障特征提取模型,用于火箭振动问题分析.构建了三维傅里叶谱,用于高效地分析全程观测信号频谱的时变趋势,研究了Wigner-Ville分布的标准算法与快速算法,用于精确刻画振动参数感兴趣数据段的频谱随时间的分布情况.试验结果表明:模型及方法处理火箭遥测数据直观、高效、精确,可以应用于实测数据分析.研究时频分析方法对试验数据进行更深层次的分析处理,对设计高可靠、高性能的运载火箭,具有重要意义.
- 运载火箭 /
- 遥测数据 /
- 故障定位 /
- 时频分析 /
- 特征提取
Abstract: To obtain more valuable information from rocket flight test data as much as possible, a fault feature extraction model was proposed to the need of rocket vibration analysis. 3D Fourier spectrum was constucted to analyze the time-varying trend of entire observation signal efficiently. Standard and fast algorithm of Wigner-Ville distribution was studied to describe the frequency spectrum of interesting data segments accurately. The model and methods were illustrated on rocket telemetry data. The results prove the model-s direct-vision, high efficiency, and accuracy. It-s of great significance for designing a rocket with high reliability and performance to analyze the flight test data more deeply, based on time-frequency analysis methods.
- rocket /
- telemetry data /
- fault location /
- time-frequency analysis /
- feature extraction

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参考文献(15)

[1]	陈以恩,李连周,唐永华,等.遥测数据处理[M].北京:国防工业出版社,2002:3-20 Chen Yien,Li Lianzhou,Tang Yonghua,et al.Telemetry data processing[M].Beijing: National Defence Industry Press,2002:3-20(in Chinese)
[2]	GJB2238A—2004 遥测数据处理[S] GJB2238A—2004 Telemetry data processing[S] (in Chinese)
[3]	Chaparro L F,Akan A.Special issue on time-frequency signal analysis and its applications[J].Journal of the Franklin Institute,2000,337(4):287-289
[4]	Samantaray L,Dash M,Panda R.A review on time-frequency,time-scale and scale-frequency domain signal analysis[J].IETE Journal of Research,2005,51(4):287-293
[5]	Stankovic S.Time-frequency analysis and its application in digital watermarking[J].EURASIP Journal on Advances in Signal Processing,2010(2):1-20
[6]	Sejdic E,Djurovic I,Jiang J.Time-frequency feature representation using energy concentration:an overview of recent advances[J].Digital Signal Processing,2009,19(1): 153-183
[7]	Ristic B,Boashash B.Kernel design for time-frequency signal analysis using the Radon transform[J].IEEE Transactions on Signal Processing,1993,41(5): 1996-2008
[8]	Barkat B,Boashash B.A high-resolution quadratic time-frequency distribution for multicomponent signals analysis [J].IEEE Transactions on Signal Processing,2001,49(10): 2232-2239
[9]	Shafi,I,Ahmad J,Shah,S I,et al.Techniques to obtain good resolution and concentrated time-frequency distributions: a review[J].EURASIP Journal on Advances in Signal Processing,2009(2):1-43
[10]	Hlawatsch F,Boudreaux-Bartels G F.Linear andquadratic time-frequency signal representations[J].IEEE Signal Processing,1992,9(2): 21-67
[11]	Jeffrey C,Patrick F.Virtues and vices of quadratic time-frequency distributions[J].IEEE Transactions on Signal Processing,2000,48(9): 2641-2650
[12]	Cohen L.Time-frequency distributions-a review[J].Proceedings of the IEEE,1989,77(7): 941-981
[13]	Boashash B,Black P J.An efficient real-time implementation of the Wigner-Ville distribution[J].IEEE Transactions on Acoustics Speech and Signal Processing,1987,35(11): 1611-1618
[14]	Xu Y N,Zhao Y,Jin C F,et al.Salient target detection based on pseudo-Wigner-Ville distribution and Renyi entropy[J].Optics Letters,2010,35(4):475-477
[15]	Climente-Alarcon V,Antonino-Daviu J A,Riera M,et al.Wigner-Ville distribution for the detection of high-order harmonics due to rotor asymmetries[C]//IEEE International Symposium on Diagnostics for Electric Machines,Power Electronics and Drives.[s.l.]:IEEE,2009:119-124