Failure Probabilities Of Basic Events Research Articles

An Interval-Valued Intuitionistic Fuzzy Bow-Tie Model (IVIF-BT) for the Effectiveness Evaluation of Safety Barriers in Natural Gas Storage Tank

Safety barriers (SBs) are important means of reducing failure risks of process systems. As barriers vary in type and function, their effectiveness needs to be evaluated in order to find a more reasonable configuration strategy. However, in practice, there is often a lack of accurate and complete data relating to SBs, which poses a significant challenge in quantitatively assessing their effectiveness. To address this issue, in this study, we propose a semi-quantitative approach for evaluating the effectiveness of both preventive and protective barriers in process systems by integrating expert elicitation, interval-valued intuitionistic fuzzy numbers (IVIFNs), and a bow-tie model. In this approach, the bow-tie model is first applied to describe the system failure scenarios and the action phases of the barriers, and then IVIFNs with expert judgment are introduced to obtain the failure probabilities of basic events and the effects of SBs. Subsequently, the effectiveness of each barrier is measured by comparing the relative change in failure risk due to the addition of the barrier. To verify the feasibility of this approach, a natural gas storage tank with some barriers was analyzed. The results show that the regular inspection of the deformation or damage of the storage tank has the highest effectiveness, followed by the installation and regular maintenance of safety electrical equipment. Furthermore, compared to a single barrier, multiple barriers can significantly reduce the system risk.

An intuitionistic fuzzy probabilistic Petri net method for risk assessment on submarine pipeline leakage failure

Under the influence of various factors, the submarine pipelines will be damaged, resulting in oil and gas leakage accidents. It is essential to perform risk assessments to prevent accidents. However, accurate and sufficient historical data usually can not be obtained. To solve this problem, this study proposes a probabilistic Petri net (PPN) method based on intuitionistic fuzzy evidential reasoning (IFER), which can determine the failure probability of basic events through domain expert experience, and quantitatively evaluate the probability of submarine pipeline leakage failure. The main contributes include: according to DNV-RP-F107, a group of intuitionistic fuzzy numbers is defined and an improved probability conversion method is proposed for defuzzification; an IFER approach is proposed, which uses belief structures as expert linguistic terms and transforms them into IFNs to deal with the uncertainty of expert evaluation; a probabilistic Petri net method is proposed to quantitatively calculate the failure probability of top event and identify the critical events. Finally, the proposed intuitionistic fuzzy probabilistic Petri net (IFPPN) method is illustrated by a case study. By comparing with the existing methods, the feasibility of this method is validated, and the evaluation results can provide a reference for the safe operation of the pipeline system.

Study on explosion risk assessment of low‐concentration gas safe combustion system based on FAHP‐fuzzy fault tree

AbstractExplosion is the most dangerous accident during direct combustion of low‐concentration gas. Few investigations have focused on explosion risk assessment of low‐concentration gas safe combustion system. This study aims to analyze explosion risk of above system. Due to insufficient accident data for calculating the failure probability of explosion, expert evaluation method and fuzzy algorithm were adopted to obtain the failure probability of basic events (BEs) and Fuzzy Analytic Hierarchy (FAHP) Process method used to improve the calculation of expert weights. Then qualitative and quantitative analysis was carried out using fault tree analysis. Results show that the failure probability of system explosion is 0.085%. Events with high‐risk probability are mainly on equipment management and personnel behavior norms. Safe combustion system has excellent performance on tempering prevention, explosion resistance and explosion relief. The outcomes of this study are expected to help the safety professionals while formulating safety management measures for low‐concentration gas safety combustion system.

Overcoming the uncertainty in a research reactor LOCA in level-1 PSA; Fuzzy based fault-tree/event-tree analysis

Probabilistic safety assessment (PSA) which plays a crucial role in risk evaluation is a quantitative approach intended to demonstrate how a nuclear reactor meets the safety margins as part of the licensing process. Despite PSA merits, some shortcomings associated with the final results exist. Conventional PSA uses crisp values to represent the failure probabilities of basic events. This causes a high level of uncertainty due to the inherent imprecision and vagueness of failure input data. In this paper, to tackle this imperfection, a fuzzy approach is employed with fault tree analysis and event tree analysis. Thus, instead of using the crisp values, a set of fuzzy numbers is applied as failure probabilities of basic events. Hence, in the fault tree and event tree analysis, the top events and the end-states frequencies are treated as fuzzy numbers. By introducing some fuzzy importance measures the critical components which contribute maximum to the system failure and total uncertainty are identified. As a practical example, under redesign Iranian heavy water research reactor loss of coolant accident is studied. The results show that the reactor protection system has the largest index in sequences lead to a core meltdown. In addition, the emergency core cooling system has a main role in preventing abnormal conditions.

Lognormal Approximations of Fault Tree Uncertainty Distributions.

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models.

A Stochastic Computational Approach for the Analysis of Fuzzy Systems

Fault tree analysis (FTA) has been widely utilized as a reliability evaluation technique for complex systems, such as nuclear power plants and aerospace systems. However, it is hard to obtain the crisp failure probabilities of basic events, owning to the insufficient information about some complex engineering systems. Hence, fuzzy set theory and fuzzy arithmetic operation (FAO) have been used as effective methods to analyze system reliability. However, it is cumbersome to evaluate complex systems based on FAO. To improve the evaluation efficiency, stochastic computational models are proposed in this paper to perform reliability analysis of a fuzzy system. Due to the features of Gaussian distribution in stochastic computation, a basic event’s failure possibility given by a fuzzy number is transformed into the expected value of it. The standard deviation of stochastic computational results gives the spread of the fuzzy number. A fuzzy system is then converted into a deterministic system. The analysis of an illustrating example shows that the proposed stochastic approach can efficiently evaluate the failure probability of a system.

A risk matrix analysis method based on potential risk influence: A case study on cryogenic liquid hydrogen filling system

A risk matrix analysis framework is proposed for risk assessment and prioritization based on potential risk influence (PRI). First, a new principle for risk level assignment considers the potential impacts of risk, including controllability, criticality, manageability and uncertainty, is established. Next, the impacts of potential risk are divided into two risk influence factors in risk matrix: probability and consequence influence factor. A fuzzy probability method is used to calculate the failure probability of basic events when appropriate reliability data is unavailable. To take the dependence of basic events into account, Bayesian belief network models established to calculate the likelihood of failure. Finally, to demonstrate the validity of the proposed method, a risk assessment and a risk ranking process are performed for a cryogenic liquid hydrogen filling system (CLHFS). The results of the case study confirmed that the proposed methodology successfully manages risk level inconsistency, and is altogether a feasible and reasonable tool for risk management.

Fault tree analysis based on TOPSIS and triangular fuzzy number

Fault tree analysis (FTA) is widely used in the failure probability evaluation of a system. The conventional failure probabilities of basic events are treated as crisp values. However, in many real applications, it is often difficult to evaluate failure probabilities of basic events from past occurrences. In order to address this issue, a new FTA method based on the technique for order preference by similarity to an ideal solution and the triangular fuzzy number is presented. Compared with the existing method, our proposed method is more efficient with less complexity.

Quantitative risk analysis of subsea pipeline and riser: an experts' assessment approach using fuzzy fault tree

Risk assessments with subsea pipeline and riser differ widely in various parts of the pipeline. In order to conduct quantitative risk analysis of subsea pipeline and riser, a practical approach of fuzzy fault tree analysis is employed. In the beginning, fuzzy fault tree of subsea pipeline and riser system is constructed. Experts’ weighting factors are worked out through analytical hierarchy process. Fuzzy fault tree based on updated model of membership function is therefore completed, obtaining fuzzy failure probability of basic events. Based on PARLOC 2001, results from the updated model are compared with Y.H. Dong’s, verifying the accuracy of the updated model. Moreover, probability of the most unexpected event, named failure of subsea pipeline and riser, are worked out. Sensitivity analysis is also completed. In summary, quantitative risk analysis of subsea pipeline and riser based on fuzzy fault tree is completed, providing some practical reference to the subsea pipeline engineering.

Dynamic accident modeling for high-sulfur natural gas gathering station

Dynamic accident modeling for a gas gathering station is implemented to prevent high-sulfur natural gas leakage and develop equipment inspection strategy. The progress of abnormal event occurring in the gas gathering station is modeled by the combination of fault tree and event sequence diagram, based on accident causal chain theory, i.e. the progress is depicted as sequential failure of safety barriers, then, the occurrence probability of the consequence of abnormal event is predicted. Consequences of abnormal events are divided into accidents and accident precursors which include incidents, near misses and so on. The Bayesian theory updates failure probability of safety barrier when a new observation (i.e. accident precursors or accidents data) arrives. Bayesian network then correspondingly updates failure probabilities of basic events of the safety barriers with the ability of abductive reasoning. Consequence occurrence probability is also updated. The results show that occurrence probability trend of different consequences and failure probability trend of safety barriers and basic events of the safety barriers can be obtained using this method. In addition, the critical basic events which play an important role in accidents occurrence are also identified. All of these provide useful information for the maintenance and inspection of the gas gathering station.

Fuzzy set theoretic approach to fault tree analysis

Research in conventional fault tree analysis (FTA) is based mainly on failure probability of basic events, which uses classical probability distributions for the failure probability of basic events. In the present paper the probabilistic consideration of basic events is replaced by possibilities, thereby leading to fuzzy fault tree analysis. Triangular and trapezoidal fuzzy numbers are used to represent the failure possibility of basic events. Since a system may have to go through different operating conditions during the design or testing phase. Thus the failure possibility of a basic event will be assigned more than one fuzzy numbers by different experts under various operating conditions. It is also well established that the selection of a fuzzy number to represent a basic event is vital in fault tree analysis. Here we developed an algorithm to find a single fuzzy number for a basic event, wherein more than one fuzzy number is assigned to that particular event. Using this algorithm, we obtain a single fuzzy number, having least variance from all fuzzy numbers assigned to the concerned event. The adequate and appropriate means and procedure for the detection of basic events having key role in the occurrence of top event in system analysis become essential. Here we have put forward an approach to rank the basic events in accordance with their importance in the occurrence of top event. This approach can be widely used to improve the reliability and to reduce the operating cost of a system. The proposed techniques are discussed and illustrated by taking an example of a nuclear power plant. Keywords: Fault tree, Triangular and Trapezoidal fuzzy number, Fuzzy importance, Ranking of fuzzy numbers

Uncertainty analysis in PSA with correlated input parameters

The probability of occurrence of top event of a fault tree in probabilistic safety assessment (PSA) is estimated from the probabilities of the basic events which constitute the fault tree. However the failure probabilities of basic events are subjected to statistical uncertainty. While analyzing the uncertainty of top event, the basic events are assumed uncorrelated or independent in most of the situation, but are not the case every time. Such statistical correlations are introduced into failure data by many causes. To handle the propagation of uncertainties in the presence of correlations, there are three methods: (i) Method of moments, (ii) P-box approach and (iii) Monte Carlo simulations. However the first two methods are difficult for implementation in large scale problems when partial correlations are present instead of fully correlated data. The paper presents a methodology based Monte Carlo simulation with Nataf Transformation of generating correlated random variables for uncertainty analysis in PSA. Computer code has been developed to implement the proposed methodology and a case study from NPP has been carried out.

Reliability Analysis of a conveyor system using hybrid data

AbstractThe quantification of a fault tree is difficult without an exact probability value for all of the basic events of the tree. To overcome this difficulty in this paper, we propose a methodology which employs ‘hybrid data’ as a tool to analyse the fault tree. The proposed methodology estimates the failure probability of basic events using the statistical analysis of field recorded failures. Under these circumstances, where there is an absence of past failure records, the method follows a fuzzy set based theoretical evaluation based on the subjective judgement of experts for the failure interval. The proposed methodology has been applied to a conveyor system. The results of the analysis reveal the effectiveness of the proposed methodology and the instrumental role played by the experience of experts in providing reliability oriented information. Copyright © 2006 John Wiley & Sons, Ltd.