Hydraulic Signal Decomposition Method based on Singular Value Decomposition

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (16) : 93-99.

Zhang Xiao-ming Tang Jian Zhang Mei-jun

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Abstract

In order to restrain the mode aliasing phenomenon and reduce noise composition, a signal decomposition method in time domain based on singular value decomposition (SVD) is proposed. Based on two features of SVD, firstly each frequency corresponds to two sizeable singular values, secondly singular values are positively related to the amplitude of its corresponded frequency. The method is conducted by adding a known simulation sine signal with an appropriate amplitude to make the location of singular values easier to be identified. Then reconstruct time series by choosing related singular values. Finally, the time series of a certain frequency can be achieved by subtracting added simulation signal. By comparing with EMD, it is effectively confirmed that the method can both eliminate mode aliasing and reduce noise composition.

Key words

singular value decomposition / construct signals / add signals / mode aliasing / noise composition

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Zhang Xiao-ming Tang Jian Zhang Mei-jun. Hydraulic Signal Decomposition Method based on Singular Value Decomposition[J]. Journal of Vibration and Shock, 2017, 36(16): 93-99

References

[1] 唐宏宾, 吴运新, 滑广军等. 基于EMD包络谱分析的液压泵故障诊断方法[J]. 振动与冲击, 2012, 31(9): 44-48.
Tang Gui-ji, Wu Yun-xin, Hua Guang-jun, et al. Fault diagnosis of pump using EMD and envelope spectrum analysis[J]. Journal of Vibration and Shock, 2012, 31(9): 44-48.
[2] Huang N. E., Shen Zheng, Long S. R., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]// Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 1998. 903-995.
[3] Huang, N. E., Wu Man-li, Qu Wen-dong, et al. Applications of Hilbert-Huang transform to non-stationary financial time
series analysis[J]. Applied Stochastic Models in Business and
Industry, 2003, 19(3): 245-268.
[4] Wu Zhaohua, Huang N. E.. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J].
Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[5] Ryan D, Kaiser J. F.. The use of a masking signal to improve empirical mode decomposition[C]// International Conference on Acousic, Speech and Signal Processing 2005 IEEE. M A, USA : MIT Press, 2005: 485-488.
[6] 赵玲, 刘小峰, 秦树人等. 消除经验模态分解中混叠现象

的改进掩膜信号法[J]. 振动与冲击, 2010, 29(9): 13-17.
ZHAO Ling, LIU Xiao-feng, QIN Shu-ren, et al. Use of masking signal to improve empirical modede composition[J]. Journal of Vibration and Shock, 2010, 29(9): 13-17.
[7] Hassanpour, H., Zehtabian, A.. Time domain signal enhancement based on an optimized singular vector denoising algorithm[J]. Digital Signal Processing, 2012, 22(5): 786-794.
[8] 刘鎏, 闫云聚, 李鹏博. 奇异谱分解在超声速无人机声振试验数据处理中的应用[J]. 振动与冲击, 2015, (03): 28-34.
LIU Liu, YAN Yun-ju, LI Peng-bo. Singular value spectral decomposition and its application in acoustic vibration test data processing of a supersonic aircraft[J]. Journal of Vibration and Shock, 2015, 34(3): 28-34.
[9] 刘敏, 张英堂, 李志宁等. 基于自适应奇异值标准谱和 EMD 的柴油机故障诊断[J]. 车用发动机, 2015, (2): 77-82.
LIU Min, ZHANG Ying-tang, Li Zhi-ning, et al. Diesel engine fault diagnosis based on adaptive sigular value stantard spectrum and empirical mode decomposition[J]. Vehicle Engine, 2015, 27(2): 77-82.
[10] 王超, 孔凡让, 黄伟国等. 改进的奇异值分解在轴承故障诊断中的应用[J]. 振动工程学报, 2014, 27(2): 296-303.
WANG Chao, KONG Fan-rang, HUANG Wei-guo, et al. Application of improved singular value decompostion in bearing fault diagnosis[J]. Journal of Vibration Engineering, 2015, (2): 77-82.
[11] 胥永刚, 谢志聪, 孟志鹏等. 基于奇异值分解的磁记忆信号特征提取方法[J]. 振动、测试与诊断, 2014, 34(6): 1105-1109.
XU Yong-gang, XIE Zhi-cong, MENG Zhi-peng, et al. Feature extraction method of magnetic memory signal based on SVD[J]. Journal of Vibration, Measurement & Diagnosis, 2014, 34(6): 1105-1109.
[12] 徐彦凯, 双凯. 自适应奇异值分解瞬变信号检测研究[J]. 电子与信息学报, 2014, 36(3): 583-588.
XU Yan-kai, SHUANG Kai. Detection of transient signal based on adaptive singular value decomposition[J]. Journal of Electronics & Information Technology, 2014, 34(6): 1105-1109.
[13] 赵学智, 叶邦彦, 陈统坚. 奇异值差分谱理论及其在车床主轴箱故障诊断中的应用[J]. 机械工程学报, 2010, 46(01): 100-108.
ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian. Difference spectrum theory of singular value and its application to the fault diagnosis of headstock of lathe[J]. Journal of Mechanical Engineering, 2010, 46(01): 100-108.
[14] 钱征文, 程礼, 李应红. 利用奇异值分解的信号降噪方法[J]. 振动、测试与诊断, 2011, 31(4): 459-463.
QIAN Zheng-wen, CHENG Li, LI Ying-hong. Noise reduction method based on singular value decomposition[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4): 459-463.