A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks

Organizational factors are the major root causes of human errors, while there have been no formal causal model of human behavior to model the effects of organizational factors on human reliability. The purpose of this paper is to develop a fuzzy Bayesian network (BN) approach to improve the quantification of organizational influences in HRA (human reliability analysis) frameworks. Firstly, a conceptual causal framework is built to analyze the causal relationships between organizational factors and human reliability or human error. Then, the probability inference model for HRA is built by combining the conceptual causal framework with BN to implement causal and diagnostic inference. Finally, a case example is presented to demonstrate the specific application of the proposed methodology. The results show that the proposed methodology of combining the conceptual causal model with BN approach can not only qualitatively model the causal relationships between organizational factors and human reliability but also can quantitatively measure human operational reliability, and identify the most likely root causes or the prioritization of root causes causing human error.     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

基于事件提取和改进MMHC的航空旅客运输事故征候贝叶斯网络建模*

为表征航空旅客运输事故征候演化机理,提出事故征候贝叶斯网络的建模方法。基于事故征候中致因事件、结果事件及分类标准的定义,以7 265起事故征候案例为样本,利用事件提取算法,识别事故征候叙述文本中的致因事件,利用改进的最大最小爬山算法实现网络建模;依据事件提取的测试集验证与结构学习的交叉验证,检验建模算法的准确性与有效性;基于证据敏感性指标,识别关键致因事件。结果表明:航空旅客运输事故征候贝叶斯网络模型包含94个节点和247条有向弧。空降冲突、严重设备故障、机组成员疾病及火灾烟雾是模型中高风险关联的致因事件,在安全监管过程中消除或减弱关键致因事件的发生能有效控制系统风险。     

基于安全屏障模型的海上钻完井作业风险分析

海上钻完井作业面临海洋环境恶劣、浅层地质灾害等复杂工况,极易发生油气泄漏、井喷等事故。为有效预防海上钻完井作业事故,提出基于瑞士奶酪模型的安全屏障模型。采用事故树和故障模式及影响分析相结合的方法,分析作业过程风险。该模型根据挪威标准D-010,建立完井作业关井阶段的物理安全屏障和安全屏障控制原理图,在此基础上构建油气泄漏事故树和失效模式与影响分析表,找出关井阶段可能的油气泄漏途径。通过对重要度计算和风险优先度值排序确定作业过程中最薄弱的安全屏障和关键故障模式。结果表明,作业过程中最薄弱的安全屏障是采油树、油管和地面控制井下安全阀(SCSSV),采油树腐蚀、密封失效、油管接头密封失效和SCSSV开关故障是影响作业过程的关键故障模式。     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

考虑组织因素的液化天然气泄漏安全评价方法

为考虑组织因素对于人因失误发生的根本性影响,提出一种以贝叶斯网络(BN)为工具的人因风险分析方法,并用于液化天然气泄漏的安全评价。先将认知可靠性与失误分析方法(CREAM)的共同绩效条件(CPC)分成3层,即组织层、技术系统层和个人层。以BN为工具,利用其因果推理和诊断推理的功能,进行人因失误预测和追溯的双向分析。将考虑组织因素的人员操作失误概率引入浮式液化天然气船(FLNGV)装卸过程LNG泄漏的BN中,进行LNG泄漏的安全评价以及事故原因的重要度分析。结果表明,"执行"失误是LNG泄漏最主要的事故原因,而个人层的CPC会对执行失误产生较大的影响。     

A safety barrier-based accident model for offshore drilling blowouts

Blowout is one of the most serious accidents in the offshore oil and gas industry. Accident records show that most of the offshore blowouts have occurred in the drilling phase. Efficient measures to prevent, mitigate, and control offshore drilling blowouts are important for the entire offshore oil and gas industry. This article proposes a new barrier-based accident model for drilling blowouts. The model is based on the three-level well control theory, and primary and secondary well control barriers and an extra well monitoring barrier are established between the reservoir and the blowout event. The three barriers are illustrated in a graphical model that is similar to the well-known Swiss cheese model. Five additional barriers are established to mitigate and control the blowout accident, and event tree analysis is used to analyze the possible consequence chains. Based on statistical data and literature reviews, failures of each barrier are presented. These failures can be used as guidance for offshore drilling operators to become aware of the vulnerabilities of the safety barrier system, and to assess the risk related to these barriers. The Macondo accident is used as a case study to show how the new model can be used to understand the development of the events leading to the accident. The model can also be used as an aid to prevent future blowouts or to stop the escalation of events.     

基于贝叶斯网络的复杂工程安全管理决策支持方法研究

为改进当前复杂工程安全风险管理中普遍存在偏重事前管理、过分依赖专家经验等不足,探讨复杂工程贝叶斯网络(BN)的构建流程,提出基于BN的安全管理辅助决策分析(DSA)方法,并以武汉越江地铁盾构隧道渗漏水事故为例进行实证研究。结果表明:利用BN能够融合多领域专家关于具体工程的实践经验,以有向图形式直观表达安全事故致因变量间的不确定性关系,在事前阶段运用正向推理技术预测事故发生概率以界定警情等级及早防范,事中运用重要度分析辨识关键致险因子以明确过程控制要点,事后运用反向推理技术快速诊断查明最可能致因组合,从而为复杂工程安全事故全过程管理提供实时辅助决策支持。     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

基于STPA和模糊BN的装配式建筑吊装施工安全风险分析*

为有效评估装配式建筑吊装施工中的安全风险状态,识别关键风险因素,运用系统理论过程分析方法(STPA)构建吊装施工过程中的控制反馈结构,识别导致危险的不安全控制行为,确定不安全控制行为的致因因素;基于风险因素间的关联关系构建贝叶斯网络（BN）模型,推理计算装配式建筑吊装施工安全风险状态概率,并结合反向诊断推理分析影响安全事故的风险因素;通过敏感性分析,识别装配式建筑吊装施工安全中的关键风险因素。结果表明:装配式建筑吊装施工安全风险处于低风险状态;起重机械超负荷运行、现场安全管理不到位和吊索吊具存在缺陷等因素是影响装配式建筑吊装施工安全风险的关键风险因素。     

Risk assessment of rare events

Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.     

Accident analysis model based on Bayesian Network and Evidential Reasoning approach

In this paper, an accident analysis model is proposed to develop the cost-efficient safety measures for preventing accidents. The model comprises two parts. In the first part, a quantitative accident analysis model is built by integrating Human Factors Analysis and Classification System (HFACS) with Bayesian Network (BN), which can be utilized to present the corresponding prevention measures. In the second part, the proposed prevention measures are ranked in a cost-effectiveness manner through Best-Fit method and Evidential Reasoning (ER) approach. A case study of vessel collision is analyzed as an illustration. The case study shows that the proposed model can be used to seek out accident causes and rank the derived safety measures from a cost-effectiveness perspective. The proposed model can provide accident investigators with a tool to generate cost-efficient safety intervention strategies.     

基于贝叶斯网络的机械安全性评估模型研究

重点阐述了基于贝叶斯网络的机械安全性评估模型的建立过程,应用贝叶斯网络建立了开式压力机安全性评估模型,对冲手事故模型中的共因失效节点进行了说明,并最终计算得出开式压力机冲手事故的发生概率.计算结果分析表明:对于系统基本事件之间有共因和相关关系的系统,BN计算结果使得事故后果概率明显增大,设计者和管理者不能忽略共因和相关关系对系统风险的影响.     

Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities

Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.     

基于BN的FTA在通用航空风险评价中的应用

针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

Measurement techniques for organizational safety causal models: Characterization and suggestions for enhancements

Through a review of literature from diverse disciplines with actual and potential application to causal modeling of organizational safety risk, this paper explores issues regarding measurement techniques in a quantitative safety analysis context. The interdependencies of modeling perspectives, constructs, and measures are indentified, leading to (a) characterization and classification of measurement techniques, (b) suggestions on the selection of appropriate measurement methods for different types of model constructs including individual-level, global, configural, and shared, and (c) discussion of the modeling implications of interactions between measurement, constructs, and causal paths. A multi-dimensional perspective is offered through combinations of different “measurement methods” and “measurement bases”. A Bayesian approach is also proposed to operationalize the multi-dimensional measurements. Examples are provided to help explain the roles of these measurements in capturing the relation between organizational factors and safety performance. This paper is a product of research which has the primary purpose of extending Probabilistic Risk Assessment (PRA) modeling frameworks to include the effects of organizational factors as the fundamental causes of accidents and incidents.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage 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.     

Offshore safety case approach and formal safety assessment of ships

PROBLEM: Tragic marine and offshore accidents have caused serious consequences including loss of lives, loss of property, and damage of the environment. METHOD: A proactive, risk-based "goal setting" regime is introduced to the marine and offshore industries to increase the level of safety. DISCUSSION: To maximize marine and offshore safety, risks need to be modeled and safety-based decisions need to be made in a logical and confident way. Risk modeling and decision-making tools need to be developed and applied in a practical environment. SUMMARY: This paper describes both the offshore safety case approach and formal safety assessment of ships in detail with particular reference to the design aspects. The current practices and the latest development in safety assessment in both the marine and offshore industries are described. The relationship between the offshore safety case approach and formal ship safety assessment is described and discussed. Three examples are used to demonstrate both the offshore safety case approach and formal ship safety assessment. The study of risk criteria in marine and offshore safety assessment is carried out. The recommendations on further work required are given. IMPACT ON INDUSTRY: This paper gives safety engineers in the marine and offshore industries an overview of the offshore safety case approach and formal ship safety assessment. The significance of moving toward a risk-based "goal setting" regime is given.