Production System Performance Identification Using Sensor Data

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 48; no. 2; pp. 255 - 264
• Main Authors: Zou, Jing, Chang, Qing, Lei, Yong, Arinez, Jorge
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Data-driven modeling; Disruption; effective disruption event identification; machine failure bottlenecks; Manufacturing systems; Mathematical model; permanent production loss; Production controls; production loss attribution; Production planning; Real time; Real-time systems; Silicon; Systems analysis
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2016.2597062

Advanced manufacturing systems are characterized by their complex dynamics, which are subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. To support daily operation, distributed sensors are utilized to monitor the status of each process. The advantages of the sensor data are not fully realized in current production system analysis due to a lack of data-driven system level modeling. Motivated by this need, we propose a data-driven manufacturing system model to describe production dynamics and develop a systematic method to identify the causes of permanent production loss. This research enables real-time production system performance diagnosis, which is invaluable in increasing system responsiveness and improving real-time production control to effectively enhance overall system efficiency.