A research on rubbing feature extraction based on information fusion and signal decomposition algorithm

To effectively identify the rotor–stator rubbing fault, the paper has brought forward a method combining principal component analysis (PCA), intrinsic time-scale decomposition (ITD), and information entropy (IE). Firstly, in considering that the characteristic information of faults extracted from the information collected by single sensor is not complete or comprehensive, the approach blends the vibration signals collected from 4 different positions at the same moment based on PCA algorithm; secondly, regarding that ITD algorithm can effectively avoid the problems of poor adaptivity and end effect, blended signals are broken down based on ITD algorithm; thirdly, calculate the IE of self-correlation function of each PRC based on the fact that the smaller IE is, the less confusion system has and the easier it is to extract fault characteristics, and treat the self-correlation function of PRC related with the minimum IE as optimal component to represent fault characteristics; fourthly, characteristic extraction of rotor–stator rubbing fault and identification are done on the basis of the frequency spectrum of optimal component. To prove the availability of method, vibration signals are subjected to validation and analysis, which are collected from different rotation speeds, casing thicknesses, rubbing positions, and types. The result indicates that the proposed PCA–ITD–IE can equally and effectively extract the characteristics of rotor–stator rubbing faults of aero-engine involved in various conditions.