Abstract
With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.
Highlights
Common cause failures (CCFs) are the dependent failures of multiple components originating from a common cause or single occurrence or condition
The system reliability is given by algebraic probability expressions of the basic components, which are quantified to include the contribution of the CCF such that the system reliability or availability with the CCF can be expressed
The CCF is modeled as a basic cause-event in the system reliability block diagram or system fault tree, appearing as the repeated input to all elements or gates affected by the CCF
Summary
Common cause failures (CCFs) are the dependent failures of multiple components originating from a common cause or single occurrence or condition. The system reliability is given by algebraic probability expressions of the basic components, which are quantified to include the contribution of the CCF such that the system reliability or availability with the CCF can be expressed This method has been studied in both binary- and multistate system (MSS) reliability [3]. The reliability of a transmission MSS considering the CCF effects has been analyzed based on Bayesian network model [18] This model can clearly express the influence of CCF on system reliability without the computation of minimum cut sets or the determination of algebraic expression of unreliability. Aiming at the research limitations mentioned above, such as the inaccuracies of parameter values and nonoverlap of common cause group, an programmed recursive method is suggested for this situation, with the benefit of reduced computational complexity for reliability and performance distribution assessment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
