Intelligent recognition for radial rubbing location of an aero-engine rotor-stator

In order to diagnose rubbing faults and improve design,it is very important to acquire the radial rubbing location of an aero-engine rotor-stator.Based on the casing vibration acceleration signals of an aero-engine rotor experimental rig,a method for the radial rubbing location identification using Laplacian eigenmaps(LE) and sphere support vector machine was investigated here.Firstly,Laplacian eigenmaps were used to extract the rubbing samples' features,their parameters were optimized with grid search method.Then,the characteristics of the samples were input to a sphere support vector machine to identify different locations of rubbing samples.Besides,with the actual rubbing data, the method was verified and compared with the corresponding results using the principal component analysis(PCA).The results showed the practicability and effectiveness of the method.