A Fuzzy Probabilistic Approach for Determining Safety Integrity Level

Sallak, Mohamed; Simon, Christophe; Aubry, Jean‐François

doi:10.1109/tfuzz.2007.903328

Cited by 62 publications

(40 citation statements)

References 16 publications

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“…Therefore, in the absence of accurate data, it may be necessary to work with rough estimates of probabilities, and the failure probabilities are treated as random variables with known probability distributions. Fault Tree Analysis FTA [23], [24] might be the only way to predict the reliability of a manufacturing milk unit when little quantitative information is available.…”

Section: Resultsmentioning

confidence: 99%

Monitoring of a Milk Manufacturing Workshop Using Chronicle and Fault Tree Approaches

Mhalla

Jerbi

Collart-Dutilleul³

et al. 2010

SIC

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Developments presented in this paper are devoted to the monitoring of manufacturing job-shops with time constraints and with assembling tasks. A new method for monitoring combining chronicles with fault tree analysis is proposed. The purpose of the proposed approach is to explain in details what is happening on the system and to help operators identifying failures in order to avoid a damage of the process or an accident with human beings. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a milk production unit is outlined. The results show that the monitoring approach allows keeping on producing, by on-line diagnosis, while providing correct quality of the manufactured products.

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Section: Resultsmentioning

confidence: 99%

Monitoring of a Milk Manufacturing Workshop Using Chronicle and Fault Tree Approaches

Mhalla

Jerbi

Collart-Dutilleul³

et al. 2010

SIC

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“…In order to compare the resulting fuzzy probability of the safety instrumented system according to the method proposed in [11] and the result obtained from our approach, we encode the fuzzy probabilities in the evidential network given in figure 11 which is equivalent to the fault tree previously given. As it can be seen, the evidential network is based on the minimal cut set obtained from the fault tree in figure 10.…”

Section: Safety Instrumented Systemsmentioning

confidence: 99%

“…As it can be seen, the evidential network is based on the minimal cut set obtained from the fault tree in figure 10. Figure 12 gives the resulting fuzzy probability obtained from the fuzzy fault tree approach [11] in a large dotted line and the fuzzy probability obtained from the evidential network in a dotted line. Due to rare event approximation the fault tree approach doesn't give the exact most likely value (KernP(SIS)) whereas the fuzzy probability obtained from the evidential network gives the exact value.…”

Section: Safety Instrumented Systemsmentioning

confidence: 99%

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Imprecise reliability by evidential networks

Simon

Weber

2009

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article deals with an implementation of probist reliability problems in evidential networks to propagate imprecise probabilities expressed as fuzzy numbers. First, the problem of imprecise knowledge in reliability problems is described concerning system and data reprsentation. Then, the basics of the evidence theory and its use in a directed acyclic graph approach are given. The imprecise probist reliability of complex system by modelling the component failure probabilities as real, interval or fuzzy numbers is pointed out. Two numerical studies of systems are done. The results are discussed and some comparisons with a Monte-Carlo simulation and a fuzzy fault tree approach are made.

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“…They are simply recalled hereafter: (4) Now, the question is where to find quality reliability data, namely: λ D , DC, β, β D and MTTR. It is worth noticing that these parameters may be subject to uncertainty, especially since SIS are highly reliable systems and produce weak historical failure data [4], [5]. Furthermore, the recourse to generic reliability data may introduce uncertainty due to lack of relevance with respect to the system under study [6].…”

Section: Introductionmentioning

confidence: 99%

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

Innal¹,

Dutuit²,

Chebila³

2013

Safety, Reliability and Risk Analysis

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Abstract-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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A Fuzzy Probabilistic Approach for Determining Safety Integrity Level

Cited by 62 publications

References 16 publications

Monitoring of a Milk Manufacturing Workshop Using Chronicle and Fault Tree Approaches

Monitoring of a Milk Manufacturing Workshop Using Chronicle and Fault Tree Approaches

Imprecise reliability by evidential networks

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

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