Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.     

Graphical fault tree analysis for fatal falls in the construction industry

The current study applied a fault tree analysis to represent the causal relationships among events and causes that contributed to fatal falls in the construction industry. Four hundred and eleven work-related fatalities in the Taiwanese construction industry were analyzed in terms of age, gender, experience, falling site, falling height, company size, and the causes for each fatality. Given that most fatal accidents involve multiple events, the current study coded up to a maximum of three causes for each fall fatality. After the Boolean algebra and minimal cut set analyses, accident causes associated with each falling site can be presented as a fault tree to provide an overview of the basic causes, which could trigger fall fatalities in the construction industry. Graphical icons were designed for each falling site along with the associated accident causes to illustrate the fault tree in a graphical manner. A graphical fault tree can improve inter-disciplinary discussion of risk management and the communication of accident causation to first line supervisors.     

一起爆破飞石事故的事故树分析

在诸多露天采场爆破事故中,飞石事故占很大的比率,对矿区的安全生产构成了严重威胁.针对一起飞石事故案例,利用系统安全分析中的事故树分析方法,通过最小割集和最小径集的求取,寻找事故发生的原因,提出爆破飞石事故的预防途径和控制措施,从而有针对性地采取安全对策,以减少事故的发生.     

Analyzing construction safety through time series methods

The construction industry produces a large amount of data on a daily basis. However, existing data sets have not been fully exploited in analyzing the safety factors of construction projects. Thus, this work describes how temporal analysis techniques can be applied to improve the safety management of construction data. Various time series (TS) methods were adopted for identifying the leading indicators or predictors of construction accidents. The data set used herein was obtained from a large construction company that is based in Singapore and contains safety inspection scores, accident cases, and project-related data collected from 2008 to 2015. Five projects with complete and sufficient data for temporal analysis were selected from the data set. The filtered data set contained 23 potential leading indicators (predictors or input variables) of accidents (output or dependent variable). TS analyses were used to identify suitable accident predictors for each of the five projects. Subsequently, the selected input variables were used to develop three different TS models for predicting accident occurrences, and the vector error correction model was found to be the best model. It had the lowest root mean squared error value for three of the five projects analyzed. This study provides insights into how construction companies can utilize TS data analysis to identify projects with high risk of accidents.     

爆破飞石产生原因事故树分析

为了避免爆破飞石伤人事故的发生,确保爆破安全,根据现场调研,运用安全分析理论查找爆破飞石产生的原因,为爆破作业设计、施工和安全管理提供科学依据。运用事故树分析法FTA(fault tree analysis)对某一爆破飞石伤人事故进行全面、系统的分析,确定引起顶上事件发生的因素,提出相应的预防措施。     

ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries

In the frame of the Accidental Risk Assessment Methodology for Industries (ARAMIS) project, this paper aims at presenting the work carried out in the part of the project devoted to the definition of accident scenarios. This topic is a key-point in risk assessment and serves as basis for the whole risk quantification.

The first result of the work is the building of a methodology for the identification of major accident hazards (MIMAH), which is carried out with the development of generic fault and event trees based on a typology of equipment and substances. The term “major accidents” must be understood as the worst accidents likely to occur on the equipment, assuming that no safety systems are installed.

A second methodology, called methodology for the identification of reference accident scenarios (MIRAS) takes into account the influence of safety systems on both the frequencies and possible consequences of accidents. This methodology leads to identify more realistic accident scenarios. The reference accident scenarios are chosen with the help of a tool called “risk matrix”, crossing the frequency and the consequences of accidents.

This paper presents both methodologies and an application on an ethylene oxide storage.     

Investigations of Human and Organizational Factors in hazardous vapor accidents

This paper presents a model to assess the contribution of Human and Organizational Factor (HOF) to accidents. The proposed model is made up of two phases. The first phase is the qualitative analysis of HOF responsible for accidents, which utilizes Human Factors Analysis and Classification System (HFACS) to seek out latent HOFs. The hierarchy of HOFs identified in the first phase provides inputs for the analysis in the second phase, which is a quantitative analysis using Bayesian Network (BN). BN enhances the ability of HFACS by allowing investigators or domain experts to measure the degree of relationships among the HOFs. In order to estimate the conditional probabilities of BN, fuzzy analytical hierarchy process and decomposition method are applied in the model. Case studies show that the model is capable of seeking out critical latent human and organizational errors and carrying out quantitative analysis of accidents. Thereafter, corresponding safety prevention measures are derived.     

基于FTA模型的荔枝冷藏运输环节风险分析

目的解决荔枝冷藏运输环节中安全风险识别及定性分析的难题。方法基于因素空间及故障树分析模型(FTA),分析荔枝冷链运输环节的安全事件集、空间结构(工位)集和简约因素集,建立荔枝冷藏运输环节风险因素关系矩阵,通过矩阵运算获取不同空间结构下荔枝运输安全事故发生的基本事件。结果根据运算求解结果,构建荔枝运输环节的故障树模型,获取了运输环节故障树的最小割集。荔枝冷藏运输事故最小割集数为13个,并分析了各个基本事件的结构重要度。结论通过研究最小割集及事件的结构重要度,进行荔枝冷链运输环节的安全分析,并提出了促进现场安全管理的对策及建议。     

Safety and reliability analysis in a polyvinyl chloride batch process using dynamic simulator-case study: Loss of containment incident

In this paper, a novel methodology in batch plant safety and reliability analysis is proposed using a dynamic simulator. A batch process involving several safety objects (e.g. sensors, controller, valves, etc.) is activated during the operational stage. The performance of the safety objects is evaluated by the dynamic simulation and a fault propagation model is generated. By using the fault propagation model, an improved fault tree analysis (FTA) method using switching signal mode (SSM) is developed for estimating the probability of failures. The timely dependent failures can be considered as unavailability of safety objects that can cause the accidents in a plant. Finally, the rank of safety object is formulated as performance index (PI) and can be estimated using the importance measures. PI shows the prioritization of safety objects that should be investigated for safety improvement program in the plants. The output of this method can be used for optimal policy in safety object improvement and maintenance. The dynamic simulator was constructed using Visual Modeler (VM, the plant simulator, developed by Omega Simulation Corp., Japan). A case study is focused on the loss of containment (LOC) incident at polyvinyl chloride (PVC) batch process which is consumed the hazardous material, vinyl chloride monomer (VCM).     

Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry

Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000–2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents.     

Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries

The strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc.--qualitative as well as quantitative--depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies.     

Hazard-Based Design of the Bow-Tie Method to Prevent and Mitigate Mine Accidents

The mining trade involves many complicated and interrelated variables—its complex environment, abundant machinery and a plethora of other contributors to accidents. In both developed and developing countries, mining accidents have caused many casualties. However, a universal risk assessment method for mining accidents is has not yet been implemented. Among risk assessment methods, the bow-tie has been used in different industry processes and systems and has proven effective. In this paper, the bow-tie model is utilized to investigate the relationship among mining accident risks, safety measures and possible consequences. The paper illustrates the hazards of mining accidents using US mine accident data. It also shows how the consequences of mine accidents are summarized by laws and regulations of different countries. This paper also introduces a series safety measures from Chinese safety standards and how the safety measures prevent and mitigate risks. At the end of the paper, a case of mine water inrush is applied using the bow-tie approach. The results show that the method is effective for analyzing mine safety.     

Assessment of fire vulnerability for nuclear power plant safety systems by combining fault-and success-oriented logic with physical models

The time behaviour of potential accident sequences may carry important information regarding nuclear power plant (NPP) safety operation and shutdown. In the case of external and environmental events, the ability of NPP components to operate correctly can be changed dramatically in a short time. In contrast to the failures caused by internal events, these two groups of undesirable events may lead to dynamic dependent failures among components of one or several systems. Such kinds of failure should be taken into account in the models of NPP behaviour. To evaluate how successfully the tasks of the safety systems will be carded out, logical models such as fault trees are usually used. The fault trees are not efficient at describing the short-term changes of the failure probabilities for system components. A method that has some advantages over the pure fault tree logic is proposed. The main features of the method are demonstrated by using examples.     

A risk analysis of winter navigation in Finnish sea areas

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish–Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40 cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.     

The Research on Subject Behavioral Risk of Whole Life-cycle Water Conservation Projects

In 2011, water conservation projects construction in China entered a stage of rapid growth. In February 2013 three dam safety accidents occurred due to improper participant subject behaviors. Improving security and risk management of water conservation projects is therefore considered as imminent. It is urgent to research the risks of the participant subject behaviors. This article takes each participating subject in the whole life-cycle of water conservation projects as the research object, analyzes the risk behaviors and risk evolution processes, dissects the cause of the risks of the participating subject behaviors on the basis of the theory of risk effect, and puts forward that reinforcing the penalties, improving criminal cost and strengthening the engineering ethics education is an effective way to solve safety problems of the current projects.     

Identification of reference accident scenarios in SEVESO establishments

In the frame of the ESREL special session on ARAMIS project, this paper aims at presenting the work carried out in the first Work Package, devoted to the definition of accident scenarios. This topic is a key-point in risk assessment, and serves as basis for the whole risk quantification. A first part of the work aims at building a Methodology for the Identification of Major Accident Hazards (MIMAH), which is carried out with the development of generic fault and event trees based on a typology of equipment and substances. This work is coupled with an historical analysis of accidents. In a second part, influence of safety devices and policies will be considered, in order to build a Methodology for the Identification of Reference Accident Scenarios (MIRAS). This last one will take into account safety systems and lead to obtain more realistic scenarios.     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.     

Common cause failure probabilities in standby safety system fault tree analysis with testing—scheme and timing dependencies

Modelling and quantification of common cause failures (CCFs) in redundant standby safety systems can be implemented by implicit or explicit fault tree techniques. Common cause event probabilities are derived for both methods for systems with time-related CCFs modelled by general multiple failure rates. The probabilities are determined so that the correct time-average risk can be obtained by a single computation. The impacts of test intervals and test staggering are included. Staggered testing is best with a certain extra-testing rule, although extra testing is not important for 1-out-of-n:G systems. An economic model provides insights into the impacts of various parameters: the optimal test interval increases with increasing redundancy and testing cost, and it decreases with increasing accident cost and initiating event rate. Staggered testing with extra tests allows for the longest optimal test intervals. A practical technique is outlined for incorporating assessment uncertainties in the estimation of multiple failure rates based on data from many plants or systems.     

基于事故树的海岛爆破飞石风险辨识

为帮助海岛爆破工程科学系统化的作业,运用事故树分析法对海岛爆破飞石事故的产生原因进行了分析,建立了爆破飞石事故树图,求出最小割集13个,最小径集8个,得到了基本事件的重要结构度值。结果表明:爆破方案审核不严、现场管理不当等问题是造成海岛爆破飞石事故的主要原因,针对这些基本事件提出了4点预防爆破飞石事故的对策措施,为今后的海岛工程爆破系统作业提供了决策支持。     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.