A common cause failure model for components under age-related degradation
Author
dc.contributor.author
Zhou, Taotao
Author
dc.contributor.author
López Droguett, Enrique
Author
dc.contributor.author
Modarres, Mohammad
Admission date
dc.date.accessioned
2020-05-05T22:28:21Z
Available date
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2020-05-05T22:28:21Z
Publication date
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2020
Cita de ítem
dc.identifier.citation
Reliability Engineering and System Safety 195 (2020) 106699
es_ES
Identifier
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10.1016/j.ress.2019.106699
Identifier
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https://repositorio.uchile.cl/handle/2250/174400
Abstract
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This paper discusses component age-related degradation and failure initiated from a shared cause and coupling factor (or mechanism) and the likelihood of the resulting common cause failure (CCF). For these components a CCF model that includes the impacts of any maintenance-related renewal is proposed. Limitations and gaps in the state-of-the-art parametric CCF models for properly handling impacts of shared causes leading to accelerated degradation and aging have been discussed. The proposed approach characterizes the likelihood of CCF based on the conventional parametric CCF model, but unlike the parametric CCF models, time-dependent CCF parameters are estimated from the degradation states including any component rejuvenation achieved through preventive maintenance. Accelerated degradation tests of three identical centrifugal pumps under shared but harsh operating conditions generated several types of sensor monitoring data until failure. Correlation between the sensor monitoring data and observed aging and pump failure times were used to infer the degradation states of the pumps tested. The results concluded that undetected shared causes that could accelerate degradation and aging, for example due to poor maintenance, could significantly affect the CCF parametric model and CCF probability. This could potentially underestimate risk estimates as the undetected components degradation accumulates. The proposed parametric CCF model would be able to determine component-specific dynamic CCF probability, for condition monitored comments using sensor information relatable to degradation and aging.
es_ES
Patrocinador
dc.description.sponsorship
US NRC grant: NRCHQ6014G0015
Center for Risk and Reliability at the University of Maryland