Title :
An Ontology Modeling Method of Mechanical Fault Diagnosis System Based on RSM
Author :
Wen, Hong ; Zhe, YinLuan ; Zhang, Huifu ; Chen, Anhua ; Liu, Deshun
Author_Institution :
Hunan Univ. of Sci. & Technol., Xiangtan, China
Abstract :
The intelligent level and diagnostic accuracy of mechanical fault diagnosis system depend on the knowledge quantity and quality in its library. While fusing existing knowledge is an important method to increase the knowledge quantity and quality in library. Accordingly, this paper using resource space model (RSM) of knowledge grid (KG) to classify and manage the fault diagnosis knowledge, then proposed an ontology modeling method of mechanical fault diagnosis system. Based on the method, we using protege 4 to construct an ontology of AC motor faults diagnosis.
Keywords :
AC motors; fault diagnosis; knowledge management; ontologies (artificial intelligence); AC motor faults diagnosis; RSM; knowledge grid; knowledge quality; knowledge quantity; library; mechanical fault diagnosis system; ontology modeling method; protege 4; resource space model; Fault diagnosis; Ontologies; mechanical fault diagnosis; ontology; protege; resource space model;
Conference_Titel :
Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
Conference_Location :
Zhuhai
Print_ISBN :
978-0-7695-3810-5
DOI :
10.1109/SKG.2009.57