A comparison study of qualitative and quantitative analysis techniques for the assessment of safety in industry

Rouvroye, J.L.; Goble, William M.; Brombacher, Aarnout; Spiker, R.Th.E.

doi:10.1007/978-1-4471-3409-1_90

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…The first computed metrics are the centre of gravity (COG) related to each fuzzy number (each line of the matrixes) and the average of the obtained COGs. Each COG is evaluated according to a discretization (with respect to dα) of (10). Therefore, the average of COGs can be deduced:…”

Section: Discussionmentioning

confidence: 99%

“…Even though, within the framework of reliability engineering and risk assessment, a large related work has been achieved, Monte Carlo analysis applied to the SIL calculation still limited. For example and not exclusive, we may note Rouvroye's Ph.D. thesis [10] in which a Monte Carlo analysis were applied to Markov model for the computation of the time dependent probability of failure on demand (PFD(t)). Also, Mechri [11] modeled the imperfect knowledge related to the common cause failure proportion (β) by a uniform and triangular probability distribution laws.…”

Section: Sis Avgmentioning

confidence: 99%

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Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Chu¹,

Chang²

2013

Safety, Reliability and Risk Analysis

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The object of this work is the probabilistic performance evaluation of safety instrumented systems (SIS), i.e. the average probability of dangerous failure on demand (PFD avg ) and the average frequency of failure (PFH), taking into account the uncertainties related to the different parameters that come into play: failure rate (λ), common cause failure proportion (β), diagnostic coverage (DC)... This leads to an accurate and safe assessment of the safety integrity level (SIL) inherent to the safety function performed by such systems. This aim is in keeping with the requirement of the IEC 61508 standard with respect to handling uncertainty. To do this, we propose an approach that combines (1) Monte Carlo simulation and (2) fuzzy sets. Indeed, the first method is appropriate where representative statistical data are available (using pdf of the relating parameters), while the latter applies in the case characterized by vague and subjective information (using membership function). The proposed approach is fully supported with a suitable computer code.

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Section: Discussionmentioning

confidence: 99%

Section: Sis Avgmentioning

confidence: 99%

Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Chu¹,

Chang²

2013

Safety, Reliability and Risk Analysis

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“…However, there are many difficulties in estimating ultra-high reliable software's failure probability. 9) Most safety-critical applications adopt fault-tolerant mechanisms in order to reduce the risk factor. If the fault-tolerant mechanisms are effective enough, the safety of the system and the plant risk will not be severely affected by a fail-ure of the processor modules.…”

Section: Cutset Analysismentioning

confidence: 99%

Plant Risk Effect Analysis Focusing on Digital I&C Equipment Failures

Kang¹,

Jang

2007

J. Nucl. Sci. Technol.

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Safety-critical digital systems have been installed in nuclear power plants and thus their safety effect evaluation has become an emerging issue. The multi-tasking feature of digital instrumentation and control (I&C) equipment could increase the risk factor because the I&C equipment affects the actuation of the safety functions in several mechanisms. In this study, we quantify the safety of the digital plant protection system in Korean nuclear power plants based on probabilistic safety assessment (PSA) technology. Fifteen fault-tree models for the digital reactor-trip system and seven for the safety-feature actuation system are constructed and integrated into the plant safety assessment model. The result of the sensitivity study shows the boundaries of a plant risk and the effect of the digital equipment failures on the total plant risk.

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Case Studies for System Reliability and Risk Assessment

Choi

Kang

Seong

Springer Series in Reliability Engineering

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A comparison study of qualitative and quantitative analysis techniques for the assessment of safety in industry

Cited by 7 publications

References 1 publication

Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Plant Risk Effect Analysis Focusing on Digital I&C Equipment Failures

Case Studies for System Reliability and Risk Assessment

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