水电站厂房多源振动信号盲分离研究

Blind separation of multi-source vibration signals in hydropower houses

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。

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.