Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies.

It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.

Fire safety barriers installed in atmospheric storage tanks have an important role in the prevention and the mitigation of accident scenarios triggered by lightning strike. The aim of the present study is the integration of the role of fire safety barriers in the probabilistic analysis of accident scenarios triggered by lightning strike on atmospheric storage tanks of flammable liquids. A statistical analysis of past similar accidents was performed to show their importance with respect to other naturel events such as floods and earthquakes. Depending on the tank type, different event trees are provided to describe the possible event sequences and consequences following lightning impact. Fault tree method was used to quantify the expected availability of fire safety barriers, which are integrated in event trees. The event tree related to external floating roof tanks and fault trees of safety barriers have been converted to an equivalent Bayesian network for performing sensitivity analysis, in order to identify the most critical basic elements of fire safety barriers that need to be improved. The application of the methodology to a real case study proved the importance of the integration of all relevant safety barriers performance and the influence of amelioration measures on the annual probability of lightning-triggered accidents.

Purpose

The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels.

Design/methodology/approach

The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed.

Findings

In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process.

Practical implications

The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately.

Originality/value

In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.

Aiming to provide a generalized method for assessing the performance of safety instrumented systems with a flexible and accurate consideration of the common cause failures’ contribution. This paper is devoted to the development of a direct way to generate the transition rate matrix associated with the continuous-time Markov model of any typical KooN architecture using any parametric model. Such a choice is considered after a detailed comparison of the ability of several dependability methods (e.g., fault trees, reliability block diagrams, Markov models, Bayesian networks, etc) to provide simple representations and genuine results in this context. To validate the developed method, the unavailability and the unconditional failure intensity of a wide range of configurations are quantified using the Binomial Failure Rate model and compared to those of the complete fault tree implementation.

The average unavailability and the average unconditional failure intensity of safety-instrumented systems represent the main performance indicators of safety integrity. This paper employs an approach based on the exploitation of the availability expression to obtain both performance measures in a simultaneous and straightforward way for any KooN configuration. The implementation of such an approach is generalized to take into account the contribution of common cause failures using any parametric model. The validation of the obtained results is verified through their application using several architectures and using Beta Factor and Binomial Failure Rate models to handle such type of dependent events. Therefore, the contribution of this paper lies in proposing one single formula that can be used to estimate the two main safety integrity’s performance indicators for any KooN architecture using any kind of common cause failures parametric model.

The high suppleness of Bayesian networks has led to their wide application in a variety of dependability modeling and analysis problems. The main objective of this paper is to extend the use of such powerful tool to estimate the occurrence frequency of failures and consequences in a straightforward way. Such extension is based on the employment of a transformation operator to substitute the original terms with matrices that hold the full dependability description of the corresponding element. Two simple case studies in reliability and safety contexts are treated using the suggested method whose results are validated through their comparison to the corresponding results of other classical dependability techniques.

Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS.

Safety Instrumented Systems (SIS) constitute an indispensable element in the process of risk reduction for almost all of nowadays' industrial facilities. The main purpose of this paper is to develop a set of generalized and simplified analytical expressions for two commonly employed metrics to assess the performance of SIS in terms of safety integrity, namely: the Average Probability of Failure on Demand (PFD_avg) and the Probability of Dangerous Failure per Hour (PFH). In addition to the capability to treat any K-out-of-N architecture, the proposed formulas can smoothly take into account the contributions of Partial Stroke Testing (PST) and Common Cause Failures (CCF). The validity of the suggested analytical expressions is ensured through various comparisons that are carried out at different stages of their construction.

Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS.