Construction of an induction machine database for a case-based reasoning system

Case-based reasoning is a field of artificial intelligence research that emphasizes the importance of specific past experiences to the solution of new problem. The ability to suggest a solution to a novel problem based on the recall and reuse of specific experiences is a tenet of the case-based reasoning paradigm. It has grown out of cognitive theories concerned with functional justifications for the dynamic properties of human memory, and is growing into a technology for expert systems. Studies examining the use of cases in design have identified a broad range of application. This approach is well suited for the engineering design domain. Cases can suggest entire or partial solutions, help decompose problems, critique proposed designs, point to opportunities or suggest relevant design criteria.The goal of this thesis is to develop a case database for induction motors and to test it using a previously developed Case Based Reasoning system. The testing results show that the CBR's adaptation mechanism system (previous work of Dr. Quang) which combines different knowledge sources and multiple reasoning paradigms can provide a dynamic environment for examining different design requirements and suggest ways for solving design problems from its routine and non-routine to creative design