Research on Time-Dependent Component Importance Measures Considering State Duration and Common Cause Failure

Abstract

Unlike the current risk monitors, Real-time Online Risk Monitoring and Management Technology is characterized by time-dependent modeling on the state duration of components. Given the real-time plant configuration, it eventually provides the time-dependent risk level and importance measures for operation and maintenance management. This paper focuses on the assessment method of time-dependent importance measures and its risk-informed applications in real-time online risk monitoring and management technology, including Fussell-Vesely (FV), risk achievement worth (RAW), and risk reduction worth (RRW). In this study, the values of component importance have been investigated with a time-dependent risk quantification model, as well as the common cause failure treatment model. Here three options of common cause failure treatment have been developed, assuming that the unavailability of a component could be due to an independent factor (Option 1), a common cause factor (Option 2), or an unconfirmed cause (Option 3). In the special case of “what if a component is out-of-service” of the RAW numerator, a hybrid method for the RAW evaluation is presented resulting in a balanced and reasonable RAW value. A simple case study was demonstrated. The results showed that the absolute values and ranking order of time-dependent importance not only reflected the effect of the cumulative state duration of component on risk, but also comprehensively accounted for all possible situations of component unavailability. Moreover, time-dependent importance measures improved and provided novel insights for online configuration management, 1) ranking SSCs/events/human actions for controlling increased risk and optimizing near–term plans; and 2) exempting or limiting temporary configurations during online operation.

Resilience Assessment: A Performance-Based Importance Measure

“…Xu et al used the values of component importance to investigate a time-dependent risk quantification model, as well as the common cause failure treatment model in operation and maintenance management. The results showed that the absolute values and ranking order of time-dependent importance reflected the effect of the cumulative state duration of component on risk and comprehensively accounted for all possible situations of component unavailability [18]. Kamra and Pahuja analyzed the substation communication network architectures using various reliability importance measures.…”