Abstract:Aimed at the wear particle recognition problem of the new Multiple Intelligent Debris Classifying System (MIDCS), data mining method was introduced in order to obtain the knowledge rules of wear particle recognition, and the expert system theory was used to realize the intelligent recognition of debris classes. A large number of typical debris caused by rolling bearing wear in the actual aeroengine operational process was obtained by MIDCS, 16 debris characteristic parameters were extracted based on the image analysis method, and the standard case library was formed. The decision tree algorithm of the Weka software was used for automatic extraction of the knowledge rules, and the knowledge rules were optimized and simplified. The extracted knowledge rules were verified and analyzed. The results show that the rules agree well with the wear particles recognition statistical laws, the extracted rules is very brief and correct, the extraction method based on Weka software can be used in the debris class recognition of MIDCS well,and the automation and intelligent level of MIDCS debris class recognition are greatly improved. It is of significant engineering value for the aeroengine rolling bearing fatigue wear fault diagnosis by using MIDCS.