Common cause failures in safety-instrumented systems: Using field experience from the petroleum industry

Hauge, Stein; Hokstad, Per; Håbrekke, Solfrid; Lundteigen, Mary Ann

doi:10.1016/j.ress.2015.09.018

Cited by 30 publications

(21 citation statements)

References 13 publications

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“…β-factor is a single parameter model proposed by Fleming in 1975 and it has gained wide acceptance in quantifying CCF in process industry because of its simplicity. A crucial assumption in the model is that whenever a common cause event occurs, all the components in that specific CCF group are assumed to fail [8,11].…”

Section: Existing Methods For Ccf Modellingmentioning

confidence: 99%

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Average probability of failure on demand estimation for burner management systems

Okubanjo

Oyetola

Groot³

et al. 2018

Nig. J. Tech.

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Proper estimation of Safety Integrity Level (SIL) depends largely on accurate estimation of Safety performance in terms of average Probability of Failure on Demand, (PFDavg). For complex architectures of logic solvers, sensors, and valves, this can be calculated by distinguishing combinations of subsystems with basic (K-out-of-N) KooN approach for identical components. In the case of the typical configurations of valves for a burner management systems with non-identical subsystem configurations the KooN approach does not apply. Hence, it becomes an issues to calculate the correct safety performance since some of the established methods give too optimistic results due to lack of Common cause Failure information and data on non-identical components or sub-systems. This paper formulates a Markov model for determination of average probability of failure on demand for non-identical components and also proposes a more conservative lowest failure rate approach and maximum beta factor contrary to pragmatic minimum or average beta for correct estimation of average probability of failure on demand. It can be deduced that the measure of safety performance for components or subsystems with unequal failure rates depends largely on common cause failure, but a single beta factor is not appropriate to model the commonality of the failure. The result revealed that both geometric mean and lowest failure rate approaches result in different values with the lowest failure rate being the most conservative and optimistic result.

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Section: Existing Methods For Ccf Modellingmentioning

confidence: 99%

“…The first three terms of Taylor's series for exponential function in equation (15)are substituted in equation [11]. After cancellation of equal terms, the 2 2 , is , ,…”

Section: Existing Methods For Ccf Modellingmentioning

confidence: 99%

Average probability of failure on demand estimation for burner management systems

Okubanjo

Oyetola

Groot³

et al. 2018

Nig. J. Tech.

View full text Add to dashboard Cite

show abstract

“…However, the intended effect may be reduced when components are subject to common cause failures. According to expert judgment, CCFs account for 1 to 10% of a component's failure rate [22]. It is consequently important to consider this kind of failure in the safety analysis in order not to overestimate the system reliability.…”

Section: Common Cause Failures (Dependent Events)mentioning

confidence: 99%

“…For reasons of simplification, we have chosen to model in the S-cube KB only groups of two or three components. In the S-cube KB, the numeric values of safety metrics were assigned based on estimations of our safety experts or based on existing studies from the literature [24] [22]. In the next section, we address the quantitative aspects related to security modeling in the Scube KB.…”

Section: Common Cause Failuresmentioning

confidence: 99%

A new safety and security risk analysis framework for industrial control systems

Kriaa

Bouissou

Laarouchi

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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The migration of modern Industrial Control Systems (ICS) towards information and communication technologies exposes them to cyber-attacks that can alter the way they function, thereby causing adverse consequences on the system and its environment. It has consequently become crucial to consider security risks in traditional safety risk analyses for industrial systems controlled by modern ICS. We propose in this paper a new framework for safety and security joint risk analysis for industrial control systems. S-cube (for SCADA Safety and Security joint modeling) is a new model-based approach that enables, thanks to a knowledge base, formal modeling of the physical and functional architecture of cyber physical systems and automatic generation of a qualitative and quantitative analysis encompassing safety risks (accidental) and security risks (malicious). We first give the principle and rationale of S-cube then we illustrate its inputs and outputs on a case study.

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“…The authors of PDS method also investigated field experience on CCFs, through presentation and derivation of generic values of β-factors for typical components in the oil and gas industry, and the demonstration of how failure data may be used to construct checklists for updating the value of β in operation, based on a review of some 12,000 maintenance notifications from six different onshore and offshore petroleum facilities [29].…”

Section: Multi-β Factor Model With Diverse Redundancymentioning

confidence: 99%

SIL verification for SRS with diverse redundancy based on system degradation using reliability block diagram

Ding

Wang

Jiang

et al. 2017

Reliability Engineering & System Safety

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Common cause failures in safety-instrumented systems: Using field experience from the petroleum industry

Cited by 30 publications

References 13 publications

Average probability of failure on demand estimation for burner management systems

Average probability of failure on demand estimation for burner management systems

A new safety and security risk analysis framework for industrial control systems

SIL verification for SRS with diverse redundancy based on system degradation using reliability block diagram

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