A contextual sensor system for non-intrusive machine status and energy monitoring

• Published in: Journal of manufacturing systems 2022-01, Vol.62, p.87-101
• Main Authors: Ren, Yutian, Li, Guann-Pyng
• Format: Article
• Language: English
• Subjects: Context awareness; Cyber physical systems; Energy disaggregation; Industrial IoT; Smart manufacturing; Worker machine interaction
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2021.11.010

•A methodology to build a contextual sensor system with prior human and documented knowledge.•Modeling the human–machine interactions in manufacturing as a correlated finite state machine.•Monitoring status and energy consumptions of components in machines with a power meter and camera.•System validation in semiconductor fabrication machines with or without automation control. Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.