Development of Intelligent Equipment Diagnosis and Maintenance System using JESS: Java Expert System Shell Technology

Development of Intelligent Equipment Diagnosis and Maintenance System using JESS: Java Expert System Shell Technology

Yin-Ho Yao (Ta Hwa Institute of Technology, Taiwan, ROC), Gilbert Y.P. Lin (National Tsing Hua University, Taiwan, ROC) and Amy J.C. Trappey (National Tsing Hua University, Taiwan, ROC)
DOI: 10.4018/978-1-59904-826-0.ch011
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Abstract

In modern manufacturing organizations, tasks of equipment and facility maintenance are complex and often geographically dispersed due to enterprise globalization. Diagnosing modern equipment breakdown requires considerable expertise and collaboration among equipment users and technical experts. Therefore, an intelligent diagnosis and maintenance system is necessary to support agile production and collaborative equipment maintenance in real time. This chapter focuses on describing 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.

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