Using Knowledge-Based Intelligent Reasoning to Support Dynamic Equipment Diagnosis and Maintenance

This research focuses on the development of a rule-based intelligent equipment trouble-shooting and maintenance platform using JAVA Expert System Shell (JESS) technology. A prototype system is designed and developed combining rule-based knowledge system and inference engine to support real-time collaborative equipment maintenance across geographical boundary. The main modules of the system include diagnosis knowledge management, project (or case) management and system administration. The knowledge management module consists of key functions such as knowledge type definition, knowledge component definition, document definition, mathematical model definition, rule and rule-set management. The project management module has key functions such as project definition, project’s role management, project’s function management and project’s rule-set execution. Further, a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) production equipment diagnosis and maintenance system is designed and implemented to demonstrate the intelligent maintenance capability. The prototype system enhances agility of TFT-LCD collaborative manufacturing processes with real-time equipment diagnosis and maintenance.