Fault Feature Extraction Method of Large Rotating Machinery

Abstract

Working condition of large rotating machine is varying. The collected vibration signals contain fault information as well as non-fault information, like load change and noise. The traditional fault feature extraction method which based on energy changing has certain limitations; therefore, new fault feature extraction method based on Fast ICA will be researched. Separating independent signals from blind signals by adopting Independent Component Analysis(ICA), purifying fault information and suppressing interference information; and the lifting wavelet packet is used for acquiring time frequency domain feature band from signals, so as to solve the difficulty that the fault information of the variable working condition rotating machine is always submerged by irregular working condition changing information such that effective fault prediction is hard to carry out.