Abstract
The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.
Highlights
The process monitoring is a critical task in all industrial plant
The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies
The multivariate control charts [Hotelling T2 control chart, multivariate cumulative SUM (CUSUM) (MCUSUM), multivariate exponentially weighted moving average (EWMA) (MEWMA)] have been used for the control of multivariate process and have proved their adequacy to reduce the complexity of such process monitoring
Summary
The process monitoring is a critical task in all industrial plant. It can be realized by the use of three principal approaches (Venkatasubramanian et al 2003): (1) the analytical methods based on mathematics models. The result of this research is a multi-agent system that applied to a multivariate process monitoring This multi-agent system uses: multivariate control chart for abnormal detection, neural network for faults diagnosis, Bayesian network for variables identification and expert system for reconfiguration task. To monitor successively the process, we suggest to use a software agent that can execute simultaneously a set of multivariate control charts and detect the process instability These different control charts are utilized in the design and implementation of the MCCEA. To regroup all the process monitoring tasks (detection, diagnosis, identification and reconfiguration) in one system, we add the RA which helps the operator to reconfigure the process after its failure It receives report from the IBNA about the variables that involved in the fault.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
