English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This report describes methods for prioritizing the risk importances of maintenances using a Probabilistic Risk Assessment (PRA). Approaches then are described for quantifying their reliability and risk effects. Two different PRA importance measures, minimal cutset importances and risk reduction importances, were used to prioritize maintenances; the findings show that both give similar results if appropriate criteria are used. The justifications for the particular importance measures also are developed. The methods developed to quantify the reliability and risk effects of maintenance actions are extensions of the usual reliability models now used in PRAs. These extended models consider degraded states of the component, and quantify the benefits of maintenance in correcting degradations and preventing failures. The negative effects of maintenance, including downtimes, also are included. These models are specific types of Markov models. The data for these models can be obtained from plant maintenance logs and from the Nuclear Plant Reliability Data System (NPRDS). To explore the potential usefulness of these models, the authors analyzed a range of postulated values of input data. These models were used to examine maintenance effects on a components reliability and performance for various maintenance programs and component data. Maintenance schedules were analyzed to optimize the component`s availability. In specific cases, the effects of maintenance were found to be large

Risk-based maintenance modeling. Prioritization of maintenance importances and quantification of maintenance effectiveness