University of California, Irvine, CA (United States); University of California, Irvine, CA (United States). California Institute for Telecommunications and Information Technology (Calit2); OSTI
University of California, Irvine, CA (United States); University of California, Irvine, CA (United States). California Institute for Telecommunications and Information Technology (Calit2)
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.
Ren, Yutian and Li, Guann-Pyng. "A contextual sensor system for non-intrusive machine status and energy monitoring." Journal of Manufacturing Systems, vol. 62, no. C, Nov. 2021. https://doi.org/10.1016/j.jmsy.2021.11.010
Ren, Yutian, & Li, Guann-Pyng (2021). A contextual sensor system for non-intrusive machine status and energy monitoring. Journal of Manufacturing Systems, 62(C). https://doi.org/10.1016/j.jmsy.2021.11.010
Ren, Yutian, and Li, Guann-Pyng, "A contextual sensor system for non-intrusive machine status and energy monitoring," Journal of Manufacturing Systems 62, no. C (2021), https://doi.org/10.1016/j.jmsy.2021.11.010
@article{osti_1977355,
author = {Ren, Yutian and Li, Guann-Pyng},
title = {A contextual sensor system for non-intrusive machine status and energy monitoring},
annote = {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.},
doi = {10.1016/j.jmsy.2021.11.010},
url = {https://www.osti.gov/biblio/1977355},
journal = {Journal of Manufacturing Systems},
issn = {ISSN 0278-6125},
number = {C},
volume = {62},
place = {United States},
publisher = {Elsevier - Society of Manufacturing Engineers},
year = {2021},
month = {11}}
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