基于WOA-VMD改进的信号盲源分离研究及应用

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (9) : 222-229.

论文

王海军1,2，郝志豪1,2，刘通3，胡航3，梁超1,2

作者信息 +

Studying and application of improved signal blind source separation based on WOA-VMD

WANG Haijun1,2, HAO Zhihao1,2, LIU Tong3, HU Hang3, LIANG Chao1,2

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文章历史 +

摘要

准确有效识别出水电站厂房振动信号的各个振源，对于水电站长期安全稳定运行有重要指导意义，盲源分离（BSS）是进行信号分解与振源识别的一种有效方法。为了实现水电站厂房复杂环境下振动信号的盲源分离，建立一种基于鲸鱼算法变分模态分解降噪（WOA-VMD）改进的信号盲源分离模型。采用WOA-VMD和相关法对观测信号进行降噪处理，确保盲源分离结果的准确性；求解多维降噪信号的协方差矩阵并进行奇异值分解，采用优势特征值法进行源数估计；最后对降噪信号进行中心化、白化前处理，通过联合近似对角化算法得到分离矩阵，实现观测信号的盲源分离。仿真结果表明，相较于传统盲源分离模型，改进模型将分离信号与源信号的相关系数分别提升了9.1%、7.1%、8.3%，分离信号主频误差也均有所降低。将该方法运用到水电站厂房振动工程实例，也取得了较好的分离效果。

Abstract

It is of great guiding significance to identify the vibration sources of the hydropower station accurately and effectively for the long-term safe and stable operation of the hydropower station. Blind source separation (BSS) is an effective method for signal decomposition and vibration source identification. In order to realize blind source separation of vibration signals under complex environment of hydropower station, an improved blind source separation model based on WOA-VMD was described. Firstly, WOA-VMD and correlation method was used to de-noise the observed signals to ensure the accuracy of blind source separation results. Then, the covariance matrix of de-noised signals was solved and the number of sources was estimated with the dominant eigenvalue method. Finally, the signals were preprocessed by centralization and whitening, and then JADE method was used to solve the separation matrix to achieve blind source separation of vibration signals. The simulation results show that compared with the traditional BSS model, the correlation coefficient between the separation signal and the source signal is improved by 9.1%, 7.1% and 8.3% respectively, and the main frequency error of the separation signal is also reduced. The method was applied to the vibration engineering of hydropower station and better separation effect was achieved.

导出引用

王海军1,2，郝志豪1,2，刘通3，胡航3，梁超1,2. 基于WOA-VMD改进的信号盲源分离研究及应用[J]. 振动与冲击, 2023, 42(9): 222-229

WANG Haijun1,2, HAO Zhihao1,2, LIU Tong3, HU Hang3, LIANG Chao1,2. Studying and application of improved signal blind source separation based on WOA-VMD[J]. Journal of Vibration and Shock, 2023, 42(9): 222-229

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