The data mining technology based on support vector machine(SVM) was introduced

and aero-engine rotor-stator rubbing positions diagnosis rule acquisition was proposed based on SVM.Firstly

the rubbing experiment of 4 rubbing parts was simulated based on aero-engine rotor tester with the casing

and a large number of experimental data were obtained by using the strain test of 4 parts of the casing.A new approach was proposed to extract knowledge rules from support vector clustering (SVC).Then SVC algorithm was adopted to get the clustering distribution matrix of the sample data with chosen features.Secondly

hyper-rectangle rules were constructed on the basis of the clustering distribution matrix.In order to make the rules more concise and explainable

hyper-rectangle rules should further simplified by using such means as rules merger

dimension reduction and interval extension.Finally

using the data mining method based on SVM

aero-engine rotor-stator rubbing positions diagnosis rules were extracted from a large number of rubbing position experiment data

then explanation and validated accordingly.The recognition rates were more than 99%

showing that the method is corrective and effective

and embodies great practical values.