Rule-based Data Mining Method In Fault Diagnosis

In accordance with the widely application of databases and data warehouses, and the reality of the installation of on-line and off-line monitoring system to significant equipment and large-scale databases and data warehouses have come into being hi fault diagnosis field, the paper represents a new method of data mining which produces visible rules. Potential knowledge can be effectively discovered by data mining method such as decision tree, rough set and association rules from a mass of fault data.Decision tree, as a flow chart, is structure of a tree, which is mostly used in finding classification rules and prediction of classification. Rough set theory is mainly used in attributes reduction and classification. Potential rule, internal relation of inaccurate or noise data can be discovered by rough set, although there is no any prior knowledge expect the data set associated with actual problem. Association rules reflect the knowledge relied or associated between one event and the other events. If associations between two attributes or several attributes are got hold, one attribute value among these attributes can be predicted by values of other attributes. When such data mining methods are used hi fault diagnosis field based on a mass of fault data about machinery state, significant information can be discovered and be showed as visible rules and decisive conclusions for diagnosis be acquired from these rules. The results indicate that rules generated by data mining system are according with actual fault features and this system can be used hi accurate classification of different faults.hi the environment of Microsoft Visual C++6.0 and Database Management System (DBMS) SQL ServerT.O, the Chinese version of Window98 operating system, a fault diagnosis system is developed by using data mining methods, hi which visible rules are produced Finally, the system is tested by experiment data of a rotaiy machinery.The innovative points of this paper are as followed: introducing the data mining methods which produce visible rules to fault diagnosis, developing the fault diagnosis system based on data mining combining with advanced database management system, using association rules and rough set and decision tree to classify faults.