A classification model based on extenics with weighted features for rolling bearing quality test

Considering the characteristics of multi-attribute decision making in analysis and detection of rolling bearing quality grades, a new model is built based on entropy theory and extension classification algorithm for analysis of rolling bearing quality grades. The qualitative description of matter element model is built with the training dataset using extension theory, and the objective weight coefficient of each characteristics is calculated based on the concept of entropy, then using correlative functions in extenics theory, the weighted correlative grade of the objects which unknown quality grades and each matter-element object in knowledge database is calculated quantitatively, and the quality classes of the object is judged according to the rules pre-set. Experimental results show that the method can realize effectively the classification of rolling bearing quality, and has the advantages such as simple model, reliable results, and high detection speed. This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.