Abstract
Reliability of safety instrumented systems (SISs) is a critical measure to ensure production safety of many industries. This paper focus on low-demand SISs. The reliability of these SISs is quantified by evaluating their probability of failure on demand (PFD). However, due to lack of knowledge, and/or vague judgments from experts, epistemic uncertainty associated with the parameters of components' degradation models is inevitable. Meanwhile, common cause failure (CCF) of two or more components caused by shared environments, often exists in SISs, reliability assessment for a SIS, therefore, becomes a challenging task. In this paper, fuzzy reliability assessment for SISs is conducted by taking account of the CCF among components of a SIS. The fuzzy Markov model is utilized to characterize the degradation process of components under fuzzy environment. The fuzzy state probability distribution of components is, then, calculated by formulating a set of constrained optimization models. Based on the fault tree of a SIS, the fuzzy PFD of the entire SIS with CCFs is formulated by using the $\beta $ -factor to quantify the CCFs. The fuzzy PFD at any $\alpha $ -cut level is, therefore, computed by a constrained optimization model. According to the optimization of parameters of SIS, the lower bound and upper bound of fuzzy PFD of SIS can be determined. Finally, we can quantify the effectiveness of CCF event for assessing the fuzzy PFD of SIS.
Highlights
Safety instrumented system (SIS) has been widely installed to respond to hazardous events in many industries, such as offshore oil production platforms and vessels
The SIS can be simplified into 7 nominal components which was characterized by fuzzy Markov model
The boundary of the state probability was identified by optimization model
Summary
Safety instrumented system (SIS) has been widely installed to respond to hazardous events in many industries, such as offshore oil production platforms and vessels. Liu and Rausand [45] used β-factor method to quantify the CCF event and assessed the reliability of SIS with three testing strategies under different demand modes by petri nets. To accurately assess the reliability of SISs, the epistemic uncertainty associated with degradation model parameters and CCFs among components are both taken into account. Based on the fault tree of the SIS and using β-factor to quantify the CCFs, the fuzzy reliability of the entire SIS is formulated and calculated by a set of constrained optimization models. The optimization models as follow use SQP to resolve
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