Intelligent Factory Agents with Predictive Analytics for Asset Management
Jay Lee; Hung-An Kao; Hossein Davari Ardakani; David Siegel NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH, USA
Abstract
The conceptual framework of predictive manufacturing systems starts with data acquisition of the monitored assets. Using appropriate sensor installations, various signals such as vibration, pressure, etc., can be extracted. In addition, historical data can be harvested for further data mining. Communication protocols, such as MTConnect and OPC, can help users record controller signals. When all the data are aggregated, this amalgamation is called “Big Data.” The transforming agent ...
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