Abstract

Abstract. We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the “myopic ” approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions. 1.

Citations

5666	Probabilistic reasoning in intelligent systems - Pearl - 1988
423	Bayesian Networks and Decision Graphs - Jensen - 2001
14	aHUGIN: A systems creating adaptive causal probabilistic networks - Olesen, Lauritzen, et al.
12	Parameter learning in object oriented Bayesian networks - Langseth, Bangsø - 2001
3	Dynamic probabilistic modelbased expert system for fault diagnosis, Computers and Chemical Engineering 24: 2473-2492 Leung - Leung, Romagnoli - 2000
1	Development of the operators current work with control and surveillance of processes and industrial plants", NUTEK's Analysis report - Olsson - 1995
1	Rommelse K - Heckerman, Breese - 1995
1	Root Cause Analysis for Pulp and Paper Applications - Weidl, Dahlquist - 2002
1	Pfeffer A - Koller - 1997