Performance-based inspection and maintenance strategies
Performance-based inspection and maintenance strategies utilize measures of equipment performance to help guide inspection and maintenance activities. A relevant measure of performance for safety system components is component unavailability. The component unavailability can also be input into a plant risk model such as a Probabilistic Risk Assessment (PRA) to determine the associated plant risk performance. Based on the present and projected unavailability performance, or the present and projected risk performance, the effectiveness of current maintenance activities can be evaluated and this information can be used to plan future maintenance activities. A significant amount of information other than downtimes or failure times is collected or can be collected when an inspection or maintenance is conducted which can be used to estimate the component unavailability. This information generally involves observations on the condition or state of the component or component piecepart. The information can be detailed such as the amount of corrosion buildup or can be general such as the general state of the component described as {open_quotes}high degradation{close_quotes}, {open_quotes}moderate degradation{close_quotes}, or {open_quotes}low degradation{close_quotes}. Much of the information collected in maintenance logs is qualitative and fuzzy. As part of an NRC Research program on performance-based engineering modeling, approaches have been developed to apply Fuzzy Set Theory to information collected on the state of the component to determine the implied component or component piecepart unavailability. Demonstrations of the applications of Fuzzy Set Theory are presented utilizing information from plant maintenance logs. The demonstrations show the power of Fuzzy Set Theory in translating engineering information to reliability and risk implications.
- Research Organization:
- Nuclear Regulatory Commission, Washington, DC (United States). Office of Nuclear Regulatory Research; Brookhaven National Lab., Upton, NY (United States)
- OSTI ID:
- 93883
- Report Number(s):
- NUREG/CP--0140-Vol.3; ON: TI95011768
- Country of Publication:
- United States
- Language:
- English
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