Development of a data pre-processing scheme and pluggable application modules for an intelligent equipment prognostics system

This paper proposes a data pre-processing scheme and pluggable application modules for an intelligent equipment prognostics system (IEPS) that can be applied to predictive maintenance of equipment in semiconductor and TFT-LCD industries. The IEPS comprises three major portions: a remote host, generic embedded devices (GEDs), and pluggable application modules. The remote host can receive data from GED for analyses, control, or prognostics. The GED can be easily embedded into diverse types of equipment to acquire equipment parameters. It has an open-standard application interface through which different prediction and analysis modules are allowed to plug in the prognostics system. The data pre-processing scheme is GUI-based and through composing data collection plans (DCPs). Specifically, the common equipment model (CEM) of equipment is collected to a graphic user interface through equipment self-description (ESD) in the GED. Then, the user can conveniently select the desired equipment parameters for the prognostics system to perform operations of analyses, control, and prediction. This paper also presents the design guide of the pluggable application modules for the user to follow to quickly develop a new PAM for equipment prognostics. By using the proposed data pre-processing scheme and pluggable application modules, the prognostics system can be generically used in diverse types of equipment without changing the system architecture.