Document: NRC Regulatory Guide
Document ID: 47b09be1-4bf8-45f9-a099-7fed871c09bd
Document Type: regulatory_guide
Title: Plant-Specific, Risk-Informed Decisionmaking: Inservice Testing (Rev. 1)
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML2114/ML21140A055.pdf
Revision Date: 2023-05
Chapter: 
Section ID: RG-1.175
CFR Part: 
CFR Title: 

Content:
ts can be detected, and compensatory measures taken, to correct the testing of the remaining population members. However, it is important that the monitoring includes enough tests to ensure that these effects will be detected early enough to be addressed, and that the tests are capable of detecting the time-related degradation. Regulatory Position C.3.3 discusses performance monitoring. A check should also be performed to determine whether non-IST manipulations have been credited either in IST basic events or in compensating component basic events. If each component is stroked or challenged between instances of IST, and if these activities are capable of revealing component failure, the effective fault exposure time can be less than the RI-IST interval and credited in the risk evaluation. In addition, some instances of revealing a component fault through challenge have adverse consequences, including functional failure. Thus, if credit is taken for shortening fault exposure time through functional challenges, the quantification of risk should account for such adverse consequences. 2.2.3.3.2 Evaluating the Change in Core Damage Frequency and Large Early Release Frequency Once the impact of the proposed change on the individual basic event probabilities has been determined, the change in CDF and LERF can be evaluated. Careful consideration should be given to issues that become more important as the change in basic event probabilities becomes larger. When using a fault tree linking approach to PRA, it is preferable that the model be re-solved rather than simply requantifying the CDF and LERF cut set solutions. In addition, it is important to pay attention to the parametric uncertainty analysis, especially if the change is dominated by cut sets that have multiple LSSCs. The “state of knowledge correlation effect” could be significant if there are a significant number of cut sets with similar SSCs contributing to the change in risk. RG 1.174 discusses the parametric