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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. [ABSTRACT FROM AUTHOR]

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing. [ABSTRACT FROM AUTHOR]