建筑物消防系统可靠性分析

为评估建筑物消防系统的可靠性,首先将系统分为探测报警系统和灭火系统等部分,再用事件树法分析事故发生时消防系统不同部分的反应情况.通过贝叶斯理论及历史统计数据得到系统失效率的不确定性概率分布,建立消防系统可靠性随时间变化的数学模型; 用蒙特卡罗方法模拟求得系统可靠性的时间函数并对模型的不确定性参数进行敏感性分析.该方法将统计数据与经验公式、理论方法相结合,并利用蒙特卡罗方法处理模型中的不确定性,不仅能够有效估计消防系统的可靠性,还可对其他类似系统的可靠性进行分析,并通过敏感性分析为进一步减少估计的不确定性提出合理建议.     

基于贝叶斯网络的航空维修人因可靠性分析

根据维修人为因素分析和分类扩展系统的框架选取影响因素,在航空维修领域应用贝叶斯网络进行人因可靠性分析,建立飞机维修效能模型,直观地表示影响因素与维修效能之间的关系。同时以目视检测为例,结合专家意见确定随机影响因素,通过专家访谈、事故报告、调查问卷、操作记录等渠道获取数据,得出条件概率表,进而建立目视检测表现模型,展示贝叶斯网络的建模流程。案例研究结果表明,组织文化、视觉信息、设备、疲劳、检测距离等因素对目视检测表现的影响非常显著,欲改善目视检测表现,必须对多影响因素进行综合管理。     

航天器总装事故原因事件重要度分析

为解决传统安全性定量分析方法不能描述航天器总装这一复杂人-机-环系统的失效数据波动性和非严格逻辑关系的问题,结合生产实际,采用问卷调查的方法找出影响总装事故的风险因素,即导致事故的事件发生可能性、输入事件对输出事件的影响程度等。在验证调查数据的有效性之后,应用基于模糊数的模糊因果图(FCD),计算某航天器与支架车连接作业的事故风险可能性。提出原因事件重要度的计算方法。通过计算发现,人员注意力和发动机保护罩作用是导致发动机损伤的关键事件。与模糊事故树(FFT)、贝叶斯网络(BN)重要度计算结果对比表明,模糊重要度计算结果能反映事故发生可能性对原因事件发生可能性值的增减的敏感度。     

地铁运营突发事件应急响应模式的马尔科夫链分析

为研究城市轨道交通网络化运营线路的风险传导规律和耦合关系,构建基于随机Petri网的同构马尔科夫链模型。通过模型分析突发事件应急响应模式中线路之间的相互影响,以及各线路启动突发事件应急响应模式对整个系统稳态的影响。结果表明,用该模型可从数学上研究城市轨道交通运营线路之间的传导规律和耦合关系,找出影响整个应急指挥系统效率的关键因素,最终提高地铁应对突发事件的能力。     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

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.     

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.     

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.     

城市河网区河流沉积物磷形态分布特征及释放贡献

以嘉兴城市河网区为研究区域,在调查区域水质和沉积物磷空间变化特征的基础上,分析沉积物中各形态磷的垂直与空间赋存状态,通过吸附释放参数计算EPC₀值,明确了沉积物-水界面磷迁移影响因素和沉积物中活跃磷素的释放风险和贡献.结果表明,研究区域水质氮磷污染较为严重,沉积物TP含量均值呈北部河网区>西部河网区>南部河网区,基于单因子指数法的生态风险评价为Ⅱ、Ⅲ和Ⅳ级的样本比例分别为10.91%、25.45%和63.64%.沉积物各形态磷整体呈现TP>IP>HCl-P>NaOH-P>OP,沉积物生物有效磷（BAP）整体呈现Olsen-P>AAP>WSP>RDP;在沉积物垂直剖面中,部分位点受到外界因素的强烈影响垂向波动较大,整体上是随深度的递增而减小,这与近年来研究区域接收的外源磷积累过程在加重有关.相关性分析表明,沉积物磷吸附容量（Q_max）与铁铝氧化物呈显著相关性 （p<0.05）;释放参数EPC₀与TP、IP与HCl-P呈显著相关性 （p<0.05）.结合BAP形态,研究区域河流沉积物NaOH-P、HCl-P、AAP、Olsen-P及WSP存在较高的释放潜力.南部河网区沉积物主要呈现磷“源”角色,西部和北部河网区部分区域沉积物充当磷“源”角色.南部河网区在长短时间尺度下的磷主要释放形态为NaOH-P、HCl-P和OP,北部河网区在长时间尺度下主要释放磷形态为NaOH-P、HCl-P,西部河网区在短时间尺度下主要释放形态以HCl-P为主,在长时间尺度下3种磷形态的释放风险仍然较高.