An alternative approach to safety integrity level determination: results from a case study

Abstract

Purpose The purpose of this paper is to validate a fuzzy risk graph model through a case study results carried out on a safety instrumented system (SIS). Design/methodology/approach The proposed model is based on an inference fuzzy system and deals with uncertainty data used as inputs of the conventional risk graph method. The coherence and redundancy of the developed fuzzy rules base are first verified in the case study. A new fuzzy model is suggested for a multi-criteria characterization of the avoidance possibility parameter. The fuzzy safety integrity level (SIL) is determined for two potential accident scenarios. Findings The applicability of the proposed fuzzy model on SIS shows the importance and pertinence of the proposed fuzzy model as decision-making tools in preventing industrial hazards while taking into consideration uncertain aspects of the data used on the conventional risk graph method. The obtained results show that the use of continuous fuzzy scales solves the problem of interpreting results and provides a more flexible structure to combine risk graph parameters. Therefore, a decision is taken on the basis of precise integrity level values and protective actions in the real world are suggested. Originality/value Fuzzy logic-based safety integrity assessment allows assessment of the SIL in a more realistic way by using the notion of the linguistic variable for representing information that is qualitative and imprecise and, therefore, ensures better decision making on risk prevention.

Construction of methods for ensuring the required level of safety integrity in the automated systems of control over technological processes

“…It is stated in paper [1] that the SIL concept was widely used in the models of industrial hazard prevention. However, in paper [2], it was generalized that the SIL is based on the probability-relying data.…”

“…However, in view of the latest research [1], the assessment of safety integrity for determining quantitative risk indicators was not performed [4] to the full extent, as the advantage is given to qualitative information and its comprehensive analysis [1]. In this case, various methods and standards [5], such as Program Evaluation and Review Technique, Monte Carlo and others are used [1]. However, they are complicated for classical analytical approaches [6].…”

“…In contrast to [1], the SIL assessment is performed when solving the problems of risk determining. During testing in the framework of this work, it was performed for separate modules MSKU-4, in particular, for the normalizer of discrete signals ND-41.…”

Multi-objective optimization of safety instrumented systems maintenance strategy: a case study

“…In order to optimize SIS performances, several works have been developed in the literature using several SIS optimization techniques, such as (Macheri, 2011; Ouazraoui and Nait Said, 2019; Brissaud et al , 2018; Sukhwinder and Bikram, 2014; Sung Kyu and Yong Soo, 2013).…”

Verification of the IEC 61508 PFH formula for 2oo3 configuration using Markov chains and Petri nets

“…In order to check the SIS effectiveness in the event of a dangerous situation, IEC 61508 was developed as a performance/risk-based standard by adopting the concept of Safety Integrity Level (SIL) (IEC 61508, 2010). Clearly, SIL is a way to measure the confidence with which the SIS can be expected to perform its intended function (safety instrumented function: SIF) satisfactorily (Dutuit et al , 2008; Ouazraoui and Nait-Said, 2019). In this context, the IEC 61508 sets two target failure measures for SIFs on the basis of how often the SIS is required to respond to hazardous events: the average probability of dangerous failure on demand (PFD avg ) for a low demand mode of operation, and the average frequency of a dangerous failure (PFH: probability of failure per hour) for high demand or a continuous mode of operation.…”