Title :
Structure Design of Intelligent Fault Diagnosis System Based on Data Mining
Author :
Bai, Chunjie ; Wu, Xiaoping ; Ye, Qing ; Song, Yexin
Author_Institution :
Dept. of Inf. Security, Naval Univ. of Eng., Wuhan
Abstract :
To solve the limitations of intelligent fault diagnosis system, a new intelligent fault diagnosis method based on data mining is presented. After a brief discussion of the general structure of intelligent fault diagnosis system based on data mining (IFDSDM), the function of its subsystem is described, followed with the structure of knowledge base and the method of knowledge representation. IFDSDM efficiently integrates the knowledge acquisition, reasoning mechanism, IKDD (improved knowledge discovery in databases system) mining and Web-based open fault diagnosis system into IFDS. Thus, IFDSDM is a new type of integrated intelligent fault diagnosis system with two networks and six bases. It improves and expands the function of conventional IFDS. What´s more, it overcomes some of the limitations of IFDS
Keywords :
Internet; data mining; diagnostic expert systems; fault diagnosis; knowledge representation; Web open fault diagnosis; data mining; intelligent fault diagnosis; knowledge acquisition; knowledge discovery; knowledge representation; reasoning mechanism; structure design; Artificial intelligence; Data engineering; Data mining; Fault diagnosis; Intelligent networks; Intelligent structures; Intelligent systems; Knowledge acquisition; Learning systems; Nonlinear dynamical systems; Data mining; Intelligent fault diagnosis; Knowledge base; Reasoning mechanism;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1714156
