Intelligent Maintenance Diagnosis System

Traditionally, the process of trouble shooting is time-consuming done by expert's personal experiences. Pre-automatic diagnosis seems a good solution to increase the service quality of trouble shooting for information technology outsourcing. Developing an expert system for pre-automatic diagnosis should be the better approach to speed up the service. The main purpose of this study is to explore how IT outsourcing service providers build a case-based reasoning expert system for trouble shooting. To solve problems, a case-based reasoning system retrieves information from one or more previous cases for reference. Then this developed system modifies the cases to adapt to the new situation. By this way, cases can be reused, and previous experiences can be retained. Finally, current cases will be combined into existing knowledge base After searching solutions for references, the study chooses the method of K-Nearest Neighbor (KNN) to classify clients' problems. This will increase the efficiency and accuracy in searching for the best solution. A pilot case study is implemented and the conclusion shows that the developed system really assists the customers effectively.