Portfolio optimization of safety measures for reducing risks in nuclear systems

Abstract

In the framework of Probabilistic Risk Assessment (PRA), we develop a method to support the selection of cost-effective portfolios of safety measures. This method provides a systemic approach to determining the optimal portfolio of safety measures that minimizes the risk of the system and thus provides an alternative to using risk importance measures for guiding the selection of safety measures. We represent combinations of events leading to system failure with Bayesian Belief Networks (BBNs) which can be derived from traditional Fault Trees (FTs) and are capable of encoding event dependencies and multi-state failure behaviours. We also develop a computationally efficient enumeration algorithm to identify which combinations (portfolios) of safety measures minimize the risk of failure at different costs of implementing the safety measures. The method is illustrated by revisiting an earlier case study concerning the airlock system of a CANDU Nuclear Power Plant (NPP). The comparison of results with those of choosing safety measures based on risk importance measures shows that our approach leads to considerably lower residual risk at different cost levels.

Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network

“…Time of the first RUL prediction u m, n (T) Unreliability at time horizon T of the edge connecting nodes m and n V Set of network nodes DM Decision Maker GA Genetic Algorithm components of the series-parallel systems are first identified through a sensitivity analysis procedure, which, however, may not be applicable to network systems; then, the resilience of the components is increased to reach the target global resilience level. This importance-driven approach for budget allocation has proven to lead to sub-optimal results ( [27]). The remainder of the paper is as follows: Section 2 summarizes the reliability model of a PHM-equipped component proposed in [20]; Section 3 describes the metrics used to quantify the impact of PHM on the overall network reliability; Section 4 shows the PDA setting; Section 5 describes the case study network and the decision alternatives; Section 6 shows the results of the PDA approach and compares them with those of some intuitive approaches; Section 7 concludes the work.…”

Building and using dynamic risk-informed diagnosis procedures for complex system accidents

“…Chakrabarti et al use event and context information for the analysis of individual devices (discrete component semi-conductors) and complex system failures (helicopter rotor transmissions). Mancuso et al 25 use PRA technology coupled with traditional fault trees that capture reactor failures that are then reflected in Bayesian belief nets. The results of their diagnostic system create portfolios of safety measures intended to address specific reactor failures.…”

Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios

“…The procedure is iterated until the budget for preventive safety measures is depleted or the risk is reduced to acceptable levels. In a recent paper [2], we showed that this iterative procedure does not necessarily lead to the optimal selection of preventive safety measures; rather, Portfolio Decision Analysis (PDA) [3] is needed to optimize the allocation of resources to the system. We therefore proposed a PDA methodology which employs Bayesian Networks (BNs) [4] to represent sequences of events that can cause accidents.…”

“…Preventive safety measures are installed at the outset of the accident scenario, thus they are not selected dynamically based on the evolving states of the system components. In the previous paper [2], the optimization model was built for static systems. In this paper, the methodology is extended to time-dependent accident scenarios by modelling Dynamic Bayesian Networks.…”