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Monitoring of complex processes with Bayesian networks

Abstract : This chapter is about the multivariate process monitoring (detection and diagnosis) with Bayesian networks. It allows to unify in a same tool (a Bayesian network) some monitoring dedicated methods like multivariate control charts or discriminant analysis. After the context introduction, we develop in section 2, principles of process monitoring, namely fault detection and fault diagnosis. We presents classical statistical techniques to achieve these tasks. In section 3, after a presentation of Bayesian networks (with discrete and Gaussian nodes), we propose the modeling of the two tasks (detection and diagnosis) in the Bayesian network framework, unifying the two steps of the process monitoring in a sole tool, the Bayesian network. An application is given in section 4 in order to demonstrate the effectiveness of the proposed approach. This application is a benchmark problem in process monitoring: the Tennessee Eastman Process. Efficiency of the network is evaluated for detection and for diagnosis. Finally, we give conclusions on the proposed approach and outlooks concerning the use of Bayesian network for the process monitoring.
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Submitted on : Monday, September 13, 2010 - 2:49:31 PM
Last modification on : Wednesday, October 20, 2021 - 3:19:19 AM
Long-term archiving on: : Tuesday, December 14, 2010 - 2:52:35 AM


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  • HAL Id : inria-00517075, version 1



Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Monitoring of complex processes with Bayesian networks. Alexander Zemliak. Bayesian Networks, Sciyo, 2010, 978-953-7619. ⟨inria-00517075⟩



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