Turbine vibration source separation based on Independent Component Analysis
Huan Ping Yang, Yibing Liu, An Hongwen, Yanbing Zhou · 2010
Independent Component Analysis (ICA) is a new method for solving the blind source separation (BBS) problem, and has been widely used in recent years in many industrial areas to decompose mixed signals into several mutual independent components. In practical work, many source mixing models are not linear model but convolution model, for which the basis linear ICA model is not suitable. In this paper we propose to use frequency domain ICA (FDICA) model to solve those problems with convolution mixing model. The observed multi-channel signals are firstly transformed to frequent domain by means of Fourier Transform, so that the convolution mixing model is changed to a linear mixing model, which can be separated with linear ICA model. A case study is showed with measured signal from the shaft vibration of a large steam turbo-generator set in a power plant. The separation with FDICA for varies signal types are discussed. The result shows that frequency domain method of ICA can separate the mixed signal clearly. The result of separation is stable in regular condition.