A novel method for risk-informed decision-making under non-ideal Instrumentation and Control conditions through the application of Bayes’ Theorem

Abstract

Instrumentation and Control systems are often assumed to break and give no readings under certain conditions, but working perfectly otherwise. In reality, aleatory and epistemic factors create a grey area where operators are often unsure of the validity of sensor measurements. Through the use of Bayes’ Theorem, this paper proposes a novel approach that first characterizes both aleatory and epistemic uncertainty, and then combines all available information in a Bayesian network, in order to produce quantitative estimates of unobservable variables in the system. Uncertainties are also propagated from sources to results in a natural manner. The approach was applied to a test case, and was able to identify a Vessel Break transient with quantitative probabilities in a timely manner despite the information being scarce, uncertain, and heterogeneous. The approach was thus demonstrated to be a possible alternative method for decision-making under such non-ideal conditions.