A data mining method that combines rough sets with decision trees to diagnose failures

data mining;  Fault diagnosis;  Association rules;  rough set;  Decision tree

According to the basic idea of data mining technology for fault diagnosis, rough sets are used to summarize data, filter redundant attributes in a large number of fault data, and obtain a simplified fault data set. Then, the decision tree classification method is used to process the refined dataset, and the rules required for classification are generated for classification, and the working steps of the method are illustrated with examples. The feasibility of this method for fault diagnosis can be seen from the examples, and finally some technical problems in the actual application process are pointed out.