期刊文献+

伪多源采样复域FastICA冲击定位算法 被引量:3

Pseudo-multi-source-sampling complex domain Fast ICA for impact location
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摘要 使用传感器阵列对材料结构冲击损伤进行定位,有助于及时发现损伤及潜在威胁,保障结构安全性。采用盲源分离(BSS)对传感器阵列信号进行预处理,提出单通道伪多源采样方法,利用每个传感器的分时段信息构建该传感器多源观测信号作为复域Fast ICA算法的输入,分离出每个传感器观测到的带有相位信息的冲击信号;结合阈值时延定位方法求解平面冲击事件的坐标。理论推导和数值仿真验证了本文设计的联合冲击定位算法的有效性。冲击定位平台上的实验结果表明,联合冲击定位算法可以从冲击-振动混合信号中分离出冲击信号,提高阵列传感器冲击定位的时延分析准确性。同时对传感器数量少于源信号的欠定BSS问题提供了一种解决方案。 Using the sensor array to locate impact can help to detect damage and its potential threats for structure materials,and to ensure structural safety. Sensor array signals are preprocessed by blind source separation( BSS). A single-channel pseudo-multi-source-sampling method is proposed,and data sequences of one sensor in different periods are used as multi-source observation signals of this sensor. The signals of all sensor arrays are used as inputs of complex domain Fast ICA impact extraction algorithm. Shock signals with phase information are separated from each sensor. The coordinates of impact events on test plane are solved by using the threshold time delay localization method. theoretical analysis and numerical simulation verify the effectiveness of the combined impact location algorithm. Experiment on the impact location platform shows that designed algorithm can separate out the impact from the signals mixed with vibration. It is suggested that the algorithm improves the precision of time delay analysis for impact location in the sensor array. It also provides a practicable solution for the underdetermined blind source separation problem while the number of sensors is less than the sources.
出处 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2016年第2期243-250,共8页 Journal of Beijing University of Aeronautics and Astronautics
基金 国家自然科学基金(51375030)~~
关键词 盲源分离(BSS) 独立分量分析 信号处理 冲击定位 单通道伪多源采样法 blind source separation(BSS) independent component analysis signal processing impact location single-channel pseudo-multi-source-sampling method
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参考文献14

  • 1DERRISO M M, CHANG F K. Future roles of structural sensing for aerospace applications : RTO-MP-AVT-141 [ R ]. Neuilly-sur- Seine : Air Force Research Lab Wright Patterson Afb Oh Air Ve- hicles Directorate ,2006.
  • 2苏永振,袁慎芳.基于独立分量分析的多源冲击定位方法[J].振动与冲击,2009,28(8):134-137. 被引量:14
  • 3HN J B,CHANG F K. Pitch-catch active sensing methods in structural health monitoring for aircraft structures[ J]. Structural Health Monitoring,2008,7( 1 ) :5-19.
  • 4耿荣生,沈功田,刘时风.基于波形分析的声发射信号处理技术[J].无损检测,2002,24(6):257-261. 被引量:60
  • 5鲍鹏宇.结构损伤监测信号处理方法研究[D].北京:北京航空航天大学,2013:65-82.
  • 6LIM Y,LEE J, OH H S, et al. Independent component regres- sion for seasonal climate prediction:An efficient way to improve muhimodel ensembles [ J ]. Theoretical and Applied Climatolo- gy,2015,119(3-4) :433-441.
  • 7ZHANG X,VIALATTE F B ,CHEN C ,et al. Embedded imple- mentation of second-order blind identification (SOBI) for real- time applications in neuroscienee[ J]. Cognitive Computation, 2015,7(S1) :56-63.
  • 8CASTELLA M, MOREAU E, ZARZOSO V. Advances in heuris- tic signal processing and applications [ M ]. Berlin: Springer, 2013 : 183-217.
  • 9COMON P,JUTTEN C. Handbook of blind source separation: Independent component analysis and applications [ M ]. New York : Academic Press ,2010 : 179-207.
  • 10BINGHAM E,HYV.~RINEN A. A fast fixed-point algorithm for independent component analysis of complex valued signals [ J ]. International Journal of Neural Systems ,2000,10 (1) :1-8.

二级参考文献8

  • 1周晚林,王鑫伟.Hilbert变换在压电智能结构冲击定位中的应用[J].振动与冲击,2004,23(3):124-127. 被引量:8
  • 2Ziola S M, Gorman M R. Source location in thin plates using cross-correlation[J]. J. Acoust. Soc. Am. 1991, 90(5 ) : 2551 - 2556.
  • 3Linsker R. A local learning rule that enables Information maximization for arbitrary input distributions [ J ]. Neural Computation, 1988,12:1661 - 1665.
  • 4Bell J, Sejnowski T J. An information-maximisation approach to blind separation and blind deconvolution[J]. Neural Computation, 1995,7(6) :11129 - 1159.
  • 5Torkkola K. Blind separation of delayed sources based on information maximization[C]. In Proc. ICASSP, Atlanta, GA, May 7 - 10, 1996.
  • 6Torkkola K. Blind separation of convolved sources based on information maximization [ C ]. Neural Networks for Signal Processing [ 1996 ] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.
  • 7耿荣生.声发射信号的波形分析技术.中国第八届声发射学术讨论会论文集[M].上海,1999,6.146-152.
  • 8刘松平,Michael Gorman,陈积懋.模态声发射检测技术[J].无损检测,2000,22(1):38-41. 被引量:32

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