Fog Computing Aided Process Monitoring System Using Benchmark System for Large-Scale Data-Driven Smart Manufacturing

Abstract

Animated by the ongoing advancement of fog computing innovation, in this paper, a fog computing aided process monitoring and control architecture is proposed for large-scale industrial processes, which empowers reliable and effective online performance optimization in each fog computing hub without changing pre-designed control subsystems. Also, a closedloop data-driven method is produced for the process monitoring system design and an adaptive arrangement approach is proposed to manage the issues brought about by the progressions of process parameters and working focuses. The possibility and viability of the proposed design approaches are checked and showed through the contextual analysis on the Tennessee Eastman (TE) benchmark system.