Event Fault Tree Analysis Research Articles

Thermal runaway safety analysis of eVTOL based on evidence theory and interval analysis

In order to ensure the safety of eVTOL, safety analysis of thermal runaway problem of lithium battery is needed. Due to insufficient failure data, a safety assessment method based on evidence theory and interval analysis is proposed. Firstly, a fault tree model for system is constructed. Secondly, for the imprecise failure rate of bottom events in fault tree analysis, the failure probability interval of bottom event is calculated by using the evidence theory, and combining with the importance analysis to address the problem of inaccurate failure rate of bottom events in fault tree analysis. The evidence theory is used to calculate the probability interval of failure of bottom events and combine it with interval theory to calculate the top events of the fault tree. Finally, the importance analysis is used to find out the basic events that have the greatest impact on the top events. The proposed method is used to analyze the safety of the eVTOL′s battery thermal runaway and calculate the importance of each bottom event, so as to provide a basis for reducing the system failure probability and improving the system safety level.

An [formula omitted]-cut interval based IF-importance measure for intuitionistic fuzzy fault tree analysis of subsea oil and gas production system

The subsea production system is very important for having deep water oil and gas production. The leakage of the oil and gas in the subsea production system may result in enormous adverse consequences on marine lives, high production loss, and serious deadly disasters to humans. For the safety analysis of subsea production system, importance measures play an important role to rank the components of the system according to their contribution to the leakage of oil and gas for preventive and corrective purposes. In conventional fault tree analysis and fuzzy fault tree analysis, various importance measures are developed for criticality analysis of the system. The objective of the present study is to develop a novel IF-importance measure by employing the concept of α-cut interval under intuitionistic fuzzy environment. The failure possibilities of basic events in fault tree analysis are quantified by triangular and trapezoidal intuitionistic fuzzy numbers. The presented research study applies α-cut intervals of intuitionistic fuzzy numbers, intuitionistic fuzzy fault tree analysis and interval arithmetic operations. The validation is carried out by comparing the developed importance measure with some pre-existing importance measures.

Covariate model-based fault tree analysis for risk assessment in chemical process industries: A case study of a chlorine manufacturing facility

Fault tree analysis (FTA) is a promising quantitative technique for risk analysis in chemical process industries (CPIs). In FTA, a certain sequence of basic events (causes) leads to one specific Top event (critical event of interest). However, the conventional fault tree analysis has the limitations of staticity and uncertainty. The staticity in conventional FTA arises due to its inability to accommodate time-dependent characteristics of the process system. Whereas uncertainty primarily lies in the failure probability data of basic events. This paper proposes an innovative methodology that uses a time-dependent covariate model to update the failure probability values of major contributing basic events in FTA. A novel subclass of the family of phase-type distributions is used to model the covariates corresponding to the basic events. The newly developed methodology is applied for a case study in a chlorine manufacturing facility to estimate the chlorine release probability. The blockage in the pipeline was identified as the significant reason for chlorine release from expert opinion and sensitivity analysis. The results of the proposed model of FTA are compared with that of conventional FTA.

Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m3 was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.

Cost-based fire risk assessment in natural gas industry by means of fuzzy FTA and ETA

Fire is the most prevalent accident in natural gas facilities. In order to assess the risk of fire in a gas processing plant, a fault tree analysis (FTA) and event tree analysis (ETA) has been developed in this paper. By utilizing FTA and ETA, the paths leading to an outcome event would be visually demonstrated. The framework was applied to a case study of processing plant in South Pars gas complex. All major underlying causes of fire accident in a gas processing facility determined through a process hazard analysis (PHA). Fuzzy logic has been employed to derive likelihood of basic events in FTA from uncertain opinion of experts. The outcome events in event tree has been simulated by computer model to evaluate their severity. In the proposed methodology the calculated risk has the unit of cost per year which allows the decision makers to discern the benefit of their investment in safety measures and risk mitigation.

Fault tree analysis as a quantitative hazard analysis with a novel method for estimating the fault probability of microbial contamination: A model food case study

A novel method was developed to estimate the fault probability of basic events in fault tree analysis (FTA) for microbial contamination. A simple food preparation process was used for the analysis. The process consisted of mixing and cutting. The fault probability (PF) was for the occasion at excess level of E. coli compared to a threshold level. The basic events were identified as faults in sanitary handling of the hands, mixing bowl, knife, and cutting board. The top event was the ultimate fault, i.e. contamination of a model food (cotton cloth). PF of the basic events was estimated, using normal distribution functions established on the basis of contamination levels, which were determined by microbiological analysis. The area under the normal distribution function to the right of a threshold level, which was the contamination level required to cause a hazard, was regarded as PF. Finally, the PF of the top event could be estimated from PF of the basic events by FTA quantitative analysis. The estimated PF of the model food proved to agree with that measured experimentally.

Reliability assessment for hybrid systems of advanced treatment units of industrial wastewater reuse using combined event tree and fuzzy fault tree analyses

Advanced treatment units (ATUs) are highly recommended for industrial wastewater reuse in the developing countries especially in arid and semi-arid areas. Reliability of a hybrid treatment system comprised of a number of individual ATUs remains blur due to lack of conceptual framework, collected data or experience in failure performance analysis of these treatment systems. This paper presents a new methodological framework for assessing reliability of hybrid system alternatives in industrial wastewater treatment by using combined event tree analysis (ETA) and fault tree analysis (FTA). The framework comprises three major steps: (1) identification of feasible alternatives; (2) reliability analysis assessment using combined FTA and ETA with fuzzy logic techniques to calculate first failure probability of individual ATUs and then reliability of each hybrid system alternative; (3) prioritisation of alternatives. Failure probability rate of events in FTA is determined by experts’ judgement. The suggested framework is demonstrated through its application to a real case study of wastewater treatment plants of industrial parks in Iran. The results show the highest failure probabilities are reverse osmosis unit with 30% and ozonation unit with 24%, while coagulation and flotation unit has the lowest failure probability of 5.4%. The most reliable alternative of hybrid system is comprised of sand filter + activated carbon + micro filter + ultra-filter + ion exchange with 74.82% reliability. Results in this study also show that selecting ATUs with higher removal efficiencies or rate of acceptable scenarios to form a hybrid ATU system cannot necessarily lead to a more reliable hybrid system without performing suggested FTA and ETA in this paper.

A Computational Framework for Prioritization of Events in Fault Tree Analysis Under Interval-Valued Probabilities

In probabilistic safety assessment (PSA), the assessed events can be prioritized using risk importance measures, which are functions of the events' failure probabilities. These probabilities can be uncertain, and consequently the resulting prioritization can be uncertain too. In this paper, we present a framework for computing the impacts of this uncertainty, which is modeled by interval-valued probabilities that establish lower and upper bounds, within which the probability may vary. Specifically, we make pairwise comparisons between events so that an event is said to dominate another if its risk importance measure is at least as high for all event probabilities that are within their respective intervals, and strictly higher for some probabilities. The dominance relations establish a partial order which can be visualized as a directed acyclic graph. We illustrate our method by analyzing the fault tree that represents the residual heat removal system of a nuclear reactor. The results for this fault tree with 31 events and 147 minimal cut sets was solved in seconds using a tailored algorithm. Theoretical properties of the algorithm suggest that much larger models can still be solved in reasonable time.

A New Ordering Method of Basic Events in Fault Tree Analysis

The ordering of basic events is critical to fault tree analysis on the basis of binary decision diagrams (BDDs). Many attempts have been made to seek an efficient ordering result with the aim of reducing the complexity of BDD. In this article, a new ordering method, namely, priority ordering method, is proposed. The new method takes into account not only the effects of the layers of fault tree but also the repeated events, the neighboring events, and the number of events under the same gate. According to these four effects, the priorities that sort the basic events of the fault tree are defined. The new method inherits the merits of structure‐based and weight‐based methods. It is able to evaluate the basic events on the basis of the structure‐based method and the size of the subtree on the basis of the weighted‐based method. Demonstrated by the examples, the proposed priority ordering method is superior to the existing ordering methods in terms of reducing the nodes in the BDD and improving the efficiency in transforming a fault tree to a BDD. Copyright © 2011 John Wiley & Sons, Ltd.

New methods to determine the importance measures of initiating and enabling events in fault tree analysis

Components' importance measures play a very important role in system reliability analysis. They are used to identify the weakest parts of the system for design improvement, failure diagnosis and maintenance. This paper deals with the problem of determining the importance measures of basic events in case of unreliability analysis of binary coherent and non-coherent fault trees. This type of analysis is typical of catastrophic top events, characterised by unacceptable consequences. Since the unreliability of systems with repairable components cannot be exactly calculated via fault tree, the Expected Number of Failures – which is obtained by integrating the unconditional failure frequency – is considered as it represents a good upper bound. In these cases it is important to classify events as initiators or enablers since their roles in the system are different, their sequence of occurrence is different and consequently they must be treated differently. New equations based on system failure frequency are described in this paper for determining the exact importance measures of initiating and enabling events. Simple examples are provided to clarify the application of the proposed calculation methods. Compared with the exact methods available in the literature, those proposed in this paper are easier to apply by hand and are simpler to implement in a fault tree analyser.