Intelligent Semantic Case Based Reasoning System for Fault Diagnosis –

Case-Based Reasoning (CBR) often shows significant promise for improving the effectiveness of decision support systems. Previous research in decision making has proposed the use of a CBR approach to accumulate, organize, preserve, link and share diverse knowledge coming from past experiences. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in decision support systems. In order to overcome the limitation of existing CBR and develop an intelligent CBR system which can not only carry out data matching retrieval, but also perform semantic associated data access, and improve the traditional keyword-based search, this paper integrates ontology technology into a CBR system and proposes to combine semantic retrieval method and numerical measurement in case retrieval. Ontology technology is an ideal selection for realizing our system thanks to the good semantic understanding offered by ontology. The resulting ontology based CBR system is applied in fault diagnosis of industrial machines, a semi-structured decision-making environment involving multiple attributes. The case illustrates the use of proposed semantic CBR system, and shows the feasibility of our approach and the benefit of the ontology support.