Aero-engine rotor-stator rubbing positions diagnosis rule acquisition based on support vector machine

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.