scholarly journals Causality Learning Approach for Supervision in the Context of Industry 4.0

Author(s):  
Kenza Amzil ◽  
Esma Yahia ◽  
Nathalie Klement ◽  
Lionel Roucoules

AbstractIn order to have a full control on their processes, companies need to ensure real time monitoring and supervision using Key Performance Indicators (KPI). KPIs serve as a powerful tool to inform about the process flow status and objectives’ achievement. Although, experts are consulted to analyze, interpret, and explain KPIs’ values in order to extensively identify all influencing factors; this does not seem completely guaranteed if they only rely on their experience. In this paper, the authors propose a generic causality learning approach for monitoring and supervision. A causality analysis of KPIs’ values is hence presented, in addition to a prioritization of their influencing factors in order to provide a decision support. A KPI prediction is also suggested so that actions can be anticipated.

2011 ◽  
Vol 314-316 ◽  
pp. 2491-2494
Author(s):  
Ying Bin Fu ◽  
Ping Yu Jiang ◽  
Yong Li

Real-time monitoring is an important function for manufacturing execution systems (MES). In order to acquire the real-time data information, RFID technology is used to collect the data in shop-floor. This paper studies RFID middleware for the real-time monitoring in discrete manufacturing. Firstly, a process flow model is proposed. And the model of the process flow is converting into a manufacturing node flow. Secondly, RFID devices are configured for the manufacturing node. And a formalized description for RFID middleware mode is suggested. For the sake of resolving the model of RFID middleware, the data process policy is studied, and the real-time status of the monitored part is acquired. At last, a prototypical software system is developed to demonstrate above ideas.


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