A common cause failure model for components under age-related degradation. (March 2020)

• Record Type: Journal Article
• Authors: Zhou, Taotao; Droguett, Enrique López; Modarres, Mohammad
• Is Part Of: Reliability engineering & system safety. Volume 195(2020)
• Publication Date: 2020-03
• Subjects: Common cause failures -- Condition monitoring -- Age -- Degradation -- Imperfect maintenance; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2019.106699
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A new model is proposed to determine component-specific dynamic CCF probability. An experimental study is conducted to elucidate the CCF impacts over service span. The risk would be underestimated as the undetected components degradation accumulates. Advances in sensing techniques and data analytics are important to enhance the current PRA research. Abstract: 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.