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ADAPTIVE RISK ASSESSMENT IN COMPLEX LARGE SCALE PROCESSES WITH REDUCED COMPUTATIONAL COMPLEXITY
(2004)
We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on the effect variables by utilizing causal probabilistic modeling. The process is modeled by Hidden Markov Models (HMM), and object oriented dynamic Bayesian networks (OOBNs). Various modeling techniques and assumptions have been used to reduce the computational complexity and ensure fast inference. The methodology is applied in a case study.