Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants
Ernie Kee and
Reliability Engineering and System Safety, 2019, vol. 190, issue C, -
Renewal process modeling is used for the failure prediction of hardware components under periodic maintenance. While most studies utilized data-driven approaches to estimate the input parameters for renewal process models, this paper initiates a line of research to integrate renewal process modeling with probabilistic models of underlying mechanisms associated with physical degradation and maintenance. At this stage of the research, the methodology integrates Markov modeling with Probabilistic Physics-of-Failure (PPoF) models of degradation, while maintenance is treated by a data-driven approach. This methodology is valuable to obtain a more accurate estimation of component reliability and availablity, especially when (i) components are highly reliable, and failure data are limited, (ii) historical data are unreliable due to changes in design, operation, and maintenance, or (iii) advanced technologies have emerged limiting operational data. The methodology explicitly incorporates the underlying spatiotemporal causes of failure into the renewal model, allowing to rank the criticality of causal factors to improve maintenance and mitigation strategies. Although the new methodology is applicable for component reliability and availability analysis in diverse industries, this paper demonstrates its value for estimating frequencies of a Loss-Of-Coolant Accident (LOCA), which is an initiating event in Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs).
Keywords: Probabilistic Risk Assessment (PRA); Renewal process modeling; Probabilistic Physics-of-Failure (PPoF); Maintenance work process model; Loss of Coolant Accident (LOCA); Component reliability and availability (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:190:y:2019:i:c:4
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