水电站厂房多源振动信号盲分离研究

引起水电站厂房结构、机组振动的振源繁多，如何有效地识别出各类振源对水电站厂房动力安全评估至关重要。将滤波去噪、源数估计和联合近似对角化方法（JADE）相结合，实现了水电站厂房多源振动信号的盲分离。首先运用滤波方法对信号去噪；然后求解多维观测信号的相关矩阵，利用优势特征值及BIC信息准则估计源信号数目；最后对信号进行预白化处理，并采用JADE方法实现振动信号的分离。模拟仿真信号验证了该组合方法的有效性。采用该方法对一大型地下水电站厂房振动信号进行了分析，准确分离出了尾水涡带、机组转动、涡壳不均匀流场等振源。研究为探究水电站厂房振源特性提供了一种方法。

Blind separation of multi-source vibration signals in hydropower houses

There exist various vibration sources in a hydropower house, and how to accurately identify them is crucial to assessing the dynamic safety of the house. In this paper, we describe a method for blind separation of the vibration sources in a large scale underground hydropower house that combines the techniques of filtering denoising, estimating the number of vibration sources, and the joint approximate diagonalization of eigen-matrix (JADE). First, a filtering denoising method is used to de-noise all the multi-dimensional signals observed. Then, the correlation matrix of the signals is solved, and the number of vibration sources is estimated using the dominant eigenvalue and Bayesian information criterion . Finally, the signals are pre-whitened, and separated using a JADE method. This blind separation method is verified through analog signal processing. When applied to the vibration signals in a hydropower house, it accurately separates vortex belt, unit rotation, and volute flow uniformity. Thus it is an effective method for exploring the characteristics of vibration sources in hydropower houses.