饮食业油烟道火灾事故概率的模糊事故树分析

根据常规事故树定量计算中基本事件概率的不确定性问题,通过引入模糊集的概念,将常规事故树中基本事件的发生概率模糊化,用三角形模糊数代替确定性发生概率,应用模糊数截集方法,推导了模糊事故树的相关算法.采用模糊事故树方法得到饮食业油烟道火灾事故发生概率,并进行了敏感性分析,找出对顶事件发生概率影响较大的基本事件,确认减小油烟道火灾事故发生的相关措施.研究表明,饮食业油烟道火灾事故概率的模糊事故树分析是切实可行的,有助于对饮食业油烟道火灾的认识、预防和扑救工作.     

Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis

Failure of oil and gas transmission pipelines was analyzed by fault tree analysis in this paper. According to failure modes of pipeline: leakage and rupture, a fault tree of the pipeline was constructed. Fifty-five minimal cut sets of the fault tree had been achieved by qualitative analysis, while the failure probability of top event and the important analyses of basic events were evaluated by quantitative analysis. In conventional fault tree analysis, probabilities of the basic events were treated as precise values, which could not reflect real situation of system because of ambiguity and imprecision of some basic events. In order to overcome this disadvantage, a new method was proposed which combined expert elicitation with fuzzy set theories to evaluate probability of the events. As an example, failure probability of pipeline installation was assessed by using the proposed method, achieving its fuzzy failure probability of 6.4603×10⁻³. The method given in this article is effective to treat fuzzy events of FTA.     

Incident tree model and incident tree analysis method for quantified risk assessment: An in-depth accident study in traffic operation

Fault tree analysis (FTA) is a logically structured process that can help identify potential causes of system failure before the failures actually occur. However, FTA often suffers from a lack of enough probabilistic basic events to check the consistency of the logic relationship among all events through linkage with gates. Sometimes, even logic relationship among all events is difficult to determine, and failures in system operation may have been experienced rarely or not at all. In order to address the limitations, this paper proposes a novel incident tree methodology that characterizes the information flow in a system instead of logical relationship, and the amount of information of a fuzzy incident instead of probability of an event. From probability statistics to fuzzy information quantities of basic incidents and accident, we propose an incident tree model and incident tree analysis (ITA) method for identification of uncertain, random, complex, possible and variable characteristic of accident occurrence in quantified risk assessment. In our research, a much detailed example for demonstrating how to create an incident tree model has been conducted by an in-depth analysis of traffic accident causation. The case study of vehicle-leaving-roadway accident with ITA illustrates that the proposed methodology may not only capture the essential information transformations of accident that occur in system operation, but also determine the various combinations of hardware faults, software failures and human errors that could result in the occurrence of specified undesired incident at the system level even accident.     

Methodology for Computer-Aided Fault Tree Analysis

Fault tree analysis is a systematic, deductive and probabilistic risk assessment tool which elucidates the causal relations leading to a given undesired event. Quantitative fault tree (failure) analysis requires a fault tree and failure data of basic events. Development of a fault tree and subsequent analysis require a great deal of expertise, which may not be available all the time. Computer-aided fault tree analysis is an easy-to-use approach, which not only provides reliable results but also facilitates the validation and repeatability of the analysis. This enhances the overall results of the fault tree analysis and quantitative risk analysis.This paper presents a revised methodology for computer-aided fault tree analysis. The methodology includes fault tree development, minimal cutsets determination, cutsets optimization and probability analysis. The methodology uses advanced concepts of fault tree development and static and dynamic modularizing for complex and large fault trees. Furthermore, it enables sensitivity analysis of the system for design modification and risk-based decision making. Application of the proposed methodology to a process system is also discussed in the paper.     

石油化工储存装置定量风险评价方法研究

提出通过基础失效概率数据库、事故树分析、事件树分析3种方式来确定重大事故的发生概率.阐述了外界气象条件和人员分布情况对风险的影响和确定方法,给出了利用区域网格方式计算装置在平面某点的个人风险叠加模型和社会风险计算方法.最后,研制开发了石油化工储存装置定量风险评价软件,利用该软件计算了某化工厂4个储存装置所产生的个人风险...     

Fuzzy fault tree analysis for fire and explosion of crude oil tanks

Crude oil tank fire and explosion (COTFE) is the most frequent type of accident in petroleum refineries, oil terminals or storage which often results in human fatality, environment pollution and economic loss. In this paper, with fault tree qualitative analysis technique, various potential causes of the COTFE are identified and a COTFE fault tree is constructed. Conventional fault tree quantitative analysis calculates the occurrence probability of the COTFE using exact probability data of the basic events. However, it is often very difficult to obtain corresponding precise data and information in advance due to insufficient data, changing environment or new components. Fuzzy set theory has been proven to be effective on such uncertain problems. Hence, this article investigates a hybrid approach of fuzzy set theory and fault tree analysis to quantify the COTFE fault tree in fuzzy environment and evaluate the COTFE occurrence probability. Further, importance analysis for the COTFE fault tree, including the Fussell–Vesely importance measure of basic events and the cut sets importance measure, is performed to help identifying the weak links of the crude oil tank system that will provide the most cost-effective mitigation. Also, a case study and analysis is provided to testify the proposed method.     

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.     

基于T-S模糊故障树的输气站场设备失效可能性研究

针对目前国内缺乏输气站场设备失效数据库的特点,同时考虑到设备具有不同的故障程度,提出将指标评价引入至T-S模糊故障树分析中,对设备进行失效可能性分析。首先构建设备的T-S模糊故障树;其次对故障树底事件进行指标评价,转化成当前工况下的故障程度;再次对设备的故障可能性值进行计算;最后,利用模糊数学的方法将故障可能性值转化为失效概率,并参考API 581中的失效概率等级对设备进行失效可能性等级划分。实例分析表明,该方法不仅比传统故障树分析更切合实际,又能够避免指标评价法淡化关键指标的不足,且兼具定量评价与半定量评价的优点。     

Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis

Vast amounts of oil & gas (O&G) are consumed around the world everyday that are mainly transported and distributed through pipelines. Only in Canada, the total length of O&G pipelines is approximately 100,000 km, which is the third largest in the world. Integrity of these pipelines is of primary interest to O&G companies, consultants, governmental agencies, consumers and other stakeholder due to adverse consequences and heavy financial losses in case of system failure. Fault tree analysis (FTA) and event tree analysis (ETA) are two graphical techniques used to perform risk analysis, where FTA represents causes (likelihood) and ETA represents consequences of a failure event. ‘Bow-tie’ is an approach that integrates a fault tree (on the left side) and an event tree (on the right side) to represent causes, threat (hazards) and consequences in a common platform. Traditional ‘bow-tie’ approach is not able to characterize model uncertainty that arises due to assumption of independence among different risk events. In this paper, in order to deal with vagueness of the data, the fuzzy logic is employed to derive fuzzy probabilities (likelihood) of basic events in fault tree and to estimate fuzzy probabilities (likelihood) of output event consequences. The study also explores how interdependencies among various factors might influence analysis results and introduces fuzzy utility value (FUV) to perform risk assessment for natural gas pipelines using triple bottom line (TBL) sustainability criteria, namely, social, environmental and economical consequences. The present study aims to help owners of transmission and distribution pipeline companies in risk management and decision-making to consider multi-dimensional consequences that may arise from pipeline failures. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process. A simple example is used to demonstrate the proposed approach.     

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.     

基于FMECA与模糊FTA的余热锅炉安全分析

为了保证余热锅炉安全运行,预防爆炸事故发生,利用FMECA方法和模糊FTA方法,研究余热锅炉事故致因。通过对余热锅炉潜在危险因素的分析辨别,探讨设备故障类型、原因及相应处理方法,引入模糊FTA对余热锅炉爆炸事故定性和定量分析,进而构建了基于FMECA与模糊FTA的余热锅炉安全分析框架,并计算得出事故发生的模糊概率以及对基本事件的敏感性分析。研究表明,余热锅炉系统的可靠度大约为96.92%,未按规定排污,报警器失灵,安全阀压力连接管堵塞及除氧器不合格事件对顶事件发生概率的影响最大,从而为该系统的安全决策提供支持。     

An extended HAZOP analysis approach with dynamic fault tree

An extended hazard and operability (HAZOP) analysis approach with dynamic fault tree is proposed to identify potential hazards in chemical plants. First, the conventional HAZOP analysis is used to identify the possible fault causes and consequences of abnormal conditions, which are called deviations. Based on HAZOP analysis results, hazard scenario models are built to explicitly represent the propagation pathway of faults. With the quantitative analysis requirements of HAZOP analysis and the time-dependent behavior of real failure events considered, the dynamic fault tree (DFT) analysis approach is then introduced to extend HAZOP analysis. To simplify the quantitative calculation, the DFT model is solved with modularization approach in which a binary decision diagram (BDD) and Markov chain approach are applied to solve static and dynamic subtrees, respectively. Subsequently, the occurrence probability of the top event and the probability importance of each basic event with respect to the top event are determined. Finally, a case study is performed to verify the effectiveness of the approach. Results indicate that compared with the conventional HAZOP approach, the proposed approach does not only identify effectively possible fault root causes but also quantitatively determines occurrence probability of the top event and the most likely fault causes. The approach can provide a reliable basis to improve process safety.     

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.     

System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment

In quantitative fault tree analysis of a system, exact failure probability values of components are utilized to calculate the failure probability of the system. However, in many real world problems, it is problematic to get precise and sufficient failure data of system components due to insufficient or imprecise information about components, changing environment or new components. A methodology has already been developed by employing fuzzy set theory for the system reliability evaluation by utilizing qualitative failure data of system components when quantitative failure data of components are inaccessible or insufficient. This paper extends the concept of fuzzy set to intuitionistic fuzzy set and proposes a novel approach to evaluate system failure probability using intuitionistic fuzzy fault tree analysis with qualitative failure data of system components. The qualitative failure data such as expert opinions are collected as linguistic terms. These linguistic terms are then quantified by triangular intuitionistic fuzzy numbers in form of membership function and non-membership function. Additionally, a method is developed for combining the different opinions of experts. To illustrate the applicability of proposed approach, a case study of the crude oil tank fire and explosion accident is performed. The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known. To help decision makers for improving the security execution of the crude oil tank system, importance measures including Fussell-Vesely importance and cut sets importance are also executed.     

模糊概率事件的故障树分析

许多职业事故的基本致因事件多属于模糊事件,其概率很难确定。本文以煤矿典型危害——内因火灾为例,探讨了模糊概率下的故障树分析问题。煤矿内因火灾故障树虽具有确定的结构形式,其基本致因事件的概率却无法统计。经过长期的生产实践,煤矿中一大批防火专家可以用自然语言较准确地描述基本事件发生的可能性,这就为故障树分析提供了有效信息。文中以模糊数学为基础,引入Delphi方法,征集并整理了25位专家对龙凤矿内因火灾致因的意见,确定了基本事件的模糊概率及其临界重要度排序,提出了行之有效的防治内因火灾措施。     

模糊故障树分析及其应用研究

基于某些实际情况下系统故障发生概率具有模糊性和不确定性的特点 ,笔者将模糊集理论引入故障树分析并针对自动化立体仓库的故障特征 ,对立体仓库各个环节发生故障时所遇到的各种模糊信息进行了科学的、定量的处理。实例证明 ,该方法是行之有效的 ;它能解决传统故障树分析不能处理同时存在随机不确定性和模糊不确定性的故障事件的问题     

基于RBF神经网络的火灾报警系统失效概率预测

报警系统失效主要包括漏报、误报,对系统进行失效概率预测,可以帮助判断设备质量优劣,评估系统效能。利用Matlab软件编程,通过神经网络预测失效概率。根据不同场所正在使用的火灾报警器的失效数据作为原始数据,归纳总结失效原因,建立事故树,结合专家打分法与模糊理论得到网络的输入值与输出值。通过网络训练,得到可以对系统失效概率进行预测的RBF神经网络,测算效率大幅提高。以70组不同品牌、用途的火灾报警系统作为算例,通过训练数据,最终达到输入底事件发生概率可直接输出顶事件发生概率的目的。结果表明,RBF神经网络相较于BP网络与事故树算得的失效概率具有更高的拟合度,RBF神经网络模型在进行系统失效概率预测时具有可靠性。     

Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station

A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process.     

An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry

Fault Tree Analysis (FTA) is an established technique in risk management associated with identified hazards specific to focused fields. It is a comprehensive, structured and logical analysis method aimed at identifying and assessing hazards of complex systems. To conduct a quantitative FTA, it is essential to have sufficient data. By considering the fact that sufficient data is not always available, the FTA method can be adopted into the problems under fuzzy environment, so called as Fuzzy Fault Tree Analysis (FFTA). This research extends FFTA methodology to petrochemical process industry in which fire, explosion and toxic gas releases are recognized as potential hazards. Specifically, the case study focuses on Deethanizer failure in petrochemical plant operations to demonstrate the proposed methodology. Consequently, the study has provided theoretical and practical values to challenge with operational data shortage in risk assessment.