Fault Diagnosis Based on Rough Sets and C4.5 Decision Tree
Meng Xiang-zhi · Journal of Northeastern University · 2006
It was found that the precision and speed of fault diagnosis is unsatisfied due to large-scale repeating data and redundant attributes in information system (decision table) during practical applications. To solve the problem,a new model based on rough sets and decision tree C4.5 is presented. The theory of rough sets as a new mathematical tool is strong at dealing with incomplete and uncertain information and used to discretize and reduce the initial sample sets,while the C4.5 decision tree is used to learn quickly the reduced decision tables and form a tree classifier. An example is given to show the whole fault diagnosis process of RH-KTB vacuum metallurgical system by use of the new model.