Fault feature separation for fault diagnosis of rotating machinery using ICA with reference
Gang Yu, Xiaohua Liang, Juan Wang · 2011
In practical situations, the vibration collected from rotating machinery is often a mixture of many vibration components and noise, therefore it is very necessary to extract fault features from the mixture first in order to achieve effective rotating machinery fault diagnosis. In this paper, independent component analysis with reference (ICA-R) method is proposed to extract the fault features using reference signals established based on the prior knowledge of machine faults, the effectiveness of the proposed approach is verified based on simulated fault signals of rotating machinery.