基于前导事件和贝叶斯网络的储罐溢油动态风险评价

部分高风险危化品企业搬迁改造困难重重,为控制风险、保护周围人民生命和财产安全,有必要建立动态风险评价系统,对事故发生概率进行监控和预测。采用贝叶斯网络对事故发生概率进行定量分析。先在利用蝴蝶结模型辨识事故原因和后果的基础上,将其转化为贝叶斯网络模型;再导入"前导事件"信息和先验概率推导后验事故发生概率,量化分析事故发生随时间的变化概率;最后,以储罐溢流场景为例进行动态风险计算,结果表明,随化工装置生产时间和"前导事件"增长,元件失效概率和事故风险呈显著增长趋势。因此建议企业应重视"前导事件"并采取措施减少"前导事件",如优化检维修方案、及时更换关键部件、进行全面的事故调查等。     

基于CREAM和贝叶斯网络的航空维修人为差错概率预测

航空维修差错不仅严重威胁着飞行安全,同时也会增加航空公司的维修成本。针对航空维修人员发生差错成因的复杂性以及历史事故数据缺乏的情况下,将人因可靠性与失误分析方法（CREAM）和贝叶斯网络（BN）相结合,提出一种改进的维修差错分析模型。根据维修任务构建相应的贝叶斯网络模型,为各子节点设置条件概率表（CPT）;基于维修基地的实际维修环境,对行为形成因子（PSFs）进行评估,得到共同绩效条件（CPCs）的水平;利用各CPC因子下各个行为功能失效模式的权重因子,对各认知活动进行失效概率的修正;将修正概率作为贝叶斯网络根节点的输入,利用推理机制,得到差错发生概率。通过案例分析和计算,验证了所述方法的可行性和有效性。     

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7265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;...     

航空维修系统危险源识别和风险分析方法

针对目前航空维修单位风险管理工作中普遍遇到的如何全面、规范、准确地识别危险源,如何科学计算和评价风险问题,提出一种危险源识别方法和风险分析方法.首先,设计维修系统的危险源引导词,并给出危险源描述方法和识别流程,以规范识别危险源;然后,设计危险源导致潜在不良后果引导词,在引导词的指引下确定危险源的所有潜在不良后果;最后,利用模糊综合评价法计算危险源导致潜在不良后果的风险.以某维修单位新增设备为例进行了危险源识别和风险计算工作,结果表明:该方法能够快速、全面、规范地找到危险源和危险源潜在不良后果,能够更为科学地计算风险,为进一步风险控制奠定基础.本方法力图使风险管理工作规范有效,并为行业内风险数据积累和分享、数据分析与预测等奠定基础.     

Risk-mitigating beliefs,risk estimates,and self-reported speeding in a sample of Australian drivers

PROBLEM: Research suggests that people who engage in risk-taking behaviors often hold specific beliefs that can mitigate or reduce their perceptions of risk associated with those behaviors. METHOD: A scale was developed (Speeding Risk Belief Scale (SRBS)) to assess beliefs about speeding-related risk and predict self-reported speeding in a random-digit telephone survey of 800 South Australian drivers between the ages of 16 and 50. RESULTS: The scale was internally consistent, and path analyses showed it to be associated with self-reported speeding, both directly and indirectly through participants' estimates of speeding-related risk. DISCUSSION: Origins of risk-mitigating beliefs and the extent to which they may be causally linked with speeding are discussed, and recommendations are made for future research. IMPACT ON INDUSTRY: This research has strong implications for the conduct of countermeasure campaigns that disseminate information on speeding-related risk.     

Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

森林航空消防风险与作战方案的综合模糊评估

为了系统研究森林航空消防中突出而复杂的安全问题,提出1种运用危险性分析法、德尔菲法、层次分析法、模糊综合评价法的复合性方法,进行森林航空消防风险分类辨识、指标体系构建与重要度评价,对给定任务场景下的拟定作战方案进行模糊综合评估,分析识别作战方案中的薄弱环节,并有针对性地提出改进优化的策略和建议。将该方法应用于某案例得到相应的评估建议：作战方案中需要改进优化的方面依次为飞机因素、飞行员因素、管理因素,其中飞机失速速度、零件耐温极限、生理状态、净空条件是要着重核查并优化的关键性指标。研究结果表明：该方法可为森林航空消防安全评估和作战方案辅助决策提供参考和支撑,具有一定的应用价值。     

基于PSR与贝叶斯网络的非常规突发事件情景分析

基于情景分析在非常规突发事件应急决策中的重要性,为了提高情景分析的有效性、全面性,在非常规突发事件"情景"界定和演变规律分析的基础上,基于PSR模型构建非常规突发事件的"压力-状态-响应"网络表达方式;利用贝叶斯网络理论,构建非常规突发事件的情景演变分析模型;以简化后的大连输油管道爆炸事件为例,示范基于PSR模型与贝叶斯网络的非常规突发事件情景分析方法的具体流程,并对推理结果进行分析。分析结果与现实情况基本一致,证明方法的可行性和有效性。实际应用中可针对具体的事件进行"多响应"、"多路径"的推理分析,以全面、系统地分析事件情景的演变情况。     

Incidence rates and risk factors for injury events in high school students in Lijin County, China

PROBLEM: Most investigations of injuries among Chinese populations are focused on urban or better developed areas, fewer on rural or poor areas. METHODS: From September 21 to 27, 2002, an epidemiological investigation of eight kinds of injury-related events was carried out among 1,944 students in 39 classes randomly sampled from middle schools in Lijin County, a rural area. The eight kinds of events were: (a) traffic accidents, (b) poisoning, (c) fall, (d) burn or scald, (e) blunt or sharp tools contact, (f) electrical contact, (g) animal or insect bites, and (h) non-fatal drowning. RESULTS: The total incidence rate of eight kinds of injuries in the previous year was 21.1%. The incidence rate among boys was 24.2%, while that among girls was 17.4%, which was significant (chi-square=13.4, P<0.05). The incidence rate among junior high school students was 23.1%, while that among senior high school students was 15.5%, which was also significant (chi-square=13.2, P<0.05). A case-control study of 410 students with at least one of eight kinds of injury events indicated that students' gender and their mothers' health status were related to their injuries. CONCLUSION: The injury incidence rate in a rural area was less than other results in urban or developed areas. Students' gender and mothers' health status were important factors associated with the occurrence of injury. IMPACT ON INDUSTRY: In order to improve the surroundings of the growing population of adolescents, some preventive measures should be put forward, especially to the high-risk students of injury.     

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.     

基于事故案例的Natech事件演化模式及风险分析

为探究非巨灾型Natech事件演化模式并降低发生风险,基于164个事故案例,归纳出6种典型演化路径,构建非巨灾型Natech事件演化模式分析框架,并基于突发事件关联网络,分析影响Natech事件风险的水平关键节点及潜在发生路径.结果表明:暴雨灾害是对全局影响力最强的节点,与人为异动的协同作用最显著;电力事故Natech...     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

Industrial disasters: Extreme events,extremely rare. Some reflections on the treatment of uncertainties in the assessment of the associated risks

This paper addresses the issue of risk from disasters occurring due to accidents in high-consequence technologies, e.g., nuclear and oil and gas. The focus is on the challenges posed to the representation and treatment of uncertainties in the assessment of such risk, given that the occurrence of such extreme disasters is extremely unlikely, and yet they occur. A general framework of analysis is proposed.     

基于动态贝叶斯网络分析法的炸药库爆炸事故分析

炸药库一旦发生燃烧爆炸事故,影响将非常恶劣,因此,以其特殊性和危险性成为安全工作的重点.在已有的对炸药库的爆炸事故进行事故树分析法(FTA)定性分析的基础上,采用贝叶斯网络转化的方法,结合优先或门、顺序相关门等动态逻辑门进行爆炸事故树的动态贝叶斯网络转化,建立了相应的动态贝叶斯网络模型,进行了定量的事故分析并得出结论.     

在“四早”原则基础上 建议加个“早研判”

新冠肺炎疫情爆发以来,各地各部门落实早发现、早报告、早隔离、早治疗的"四早"原则。国家减灾委专家委副主任、国务院应急管理专家组组长闪淳昌建议,在"四早"原则基础上还应加个"早研判",将"五早"原则贯穿在应对突发事件的全过程。     

Utility estimates of job performance as related to the Data,People, and Things parameters of work

The purpose of this study was to investigate the applicability of the Schmidt–Hunter global estimation method of assessing utility across a broad range of jobs. Ninety-five industrial/organizational psychologists estimated the utility of performance in 24 carefully selected jobs. The utility estimates were compared to the Data, People, and Things worker function parameters from the Dictionary of Occupational Titles. The majority of jobs (62.5 per cent) produced significantly positively skewed distributions of dollar-valued job performance. The Data, People, and Things parameters accounted for 46 per cent of the variance in the non-symmetry of the utility distributions and 33 per cent of the variance in the inter-rater reliability of the utility estimates. The implications of the results were discussed for utility assessment, and explained from a decision theoretic perspective. © 1998 John Wiley & Sons, Ltd.     

Working conditions,psychological/physical symptoms and occupational accidents. Bayesian network models

Each day thousands of workers suffer occupational accidents of varying degrees of severity. Accidents at work render workers incapable of carrying out their day to day activities, either temporarily or permanently, and they also have detrimental effects on family life, the company, and the general public. In order to reduce the occupational accident rate, it is necessary to determine the causes of those accidents. Although there are many different types of accidents, they generally stem from poor working conditions. The purpose of this study was to analyze the influence of working conditions on occupational accidents from data gathered in the VI National Survey of Working Conditions (VI NSWCs) in 2007. This survey utilized a random sample of the active population of Spain. The sample comprised 11,054 people (5917 males and 5137 females). In order to carry out the study, a probabilistic model was built using Bayesian networks. The model included the following variables: hygiene conditions, ergonomic conditions, job demands, physical symptoms, psychological symptoms, and occupational accidents. The study demonstrated that there were strong relationships between hygiene conditions and occupational accidents; it has been shown that poor hygienic conditions duplicate the probability of accident. Physical symptoms increased almost 50% due to poor ergonomic conditions. And finally, high job demands almost duplicated the psychological symptoms. The investigation also showed a high degree of interdependence between physical and psychological symptomatologies and the relationship between these and occupational accidents.     

Bayesian networks make LOPA more effective,QRA more transparent and flexible,and thus safety more definable!

Quantitative risk analysis is in principle an ideal method to map one’s risks, but it has limitations due to the complexity of models, scarcity of data, remaining uncertainties, and above all because effort, cost, and time requirements are heavy. Also, software is not cheap, the calculations are not quite transparent, and the flexibility to look at various scenarios and at preventive and protective options is limited. So, the method is considered as a last resort for determination of risks. Simpler methods such as LOPA that focus on a particular scenario and assessment of protection for a defined initiating event are more popular. LOPA may however not cover the whole range of credible scenarios, and calamitous surprises may emerge.In the past few decades, Artificial Intelligence university groups, such as the Decision Systems Laboratory of the University of Pittsburgh, have developed Bayesian approaches to support decision making in situations where one has to weigh gains and costs versus risks. This paper will describe details of such an approach and will provide some examples of both discrete random variables, such as the probability values in a LOPA, and continuous distributions, which can better reflect the uncertainty in data.