An Application of Bayesian Inference on the Modeling and Estimation of Operational Risk Using Banking Loss Data

Bayesian inference method has been presented in this paper for the modeling of operational risk. Bank internal and external data are divided into defined loss cells and then fitted into probability distributions. The distribution parameters and their uncertainties are estimated from posterior distributions derived using the Bayesian inference. Loss frequency is fitted into Poisson distributions. While the Poisson parameters, in a similar way, are defined by a posterior distribution developed using Bayesian inference. Bank operation loss typically has some low frequency but high magnitude loss data. These heavy tail low frequency loss data are divided into several buckets where the bucket frequencies are defined by the experts. A probability distribution, as defined by the internal and external data, is used for these data. A Poisson distribution is used for the bucket frequencies. However instead of using any distribution of the Poisson parameters, point estimations are used. Monte Carlo simulation is then carried out to calculate the capital charge of the in- ternal as well as the heavy tail high profile low frequency losses. The output of the Monte Carlo simulation defines the capital requirement that has to be allocated to cover potential operational risk losses for the next year.

Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network

In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately.

Development of Bayesian Network Models for Risk-Based Ship Design

在过去的十五年里，轮船安全处理的注意开始通过整个海上的工业作为一个目的而非限制扫了。基于风险的轮船设计(RBD ) 方法论，在常规设计过程以内倡导风险评价的系统的集成开始了到起飞。尽管有这宽识别和增加的流行，能潜在地破坏结果的质量的重要因素在风险评价过程期间来自量、质的方面。这份报纸由通过与数据通过第一原则的途径产生了相结合的历史的数据的有效存储并且处理为风险评价开发形式化的方法论详细说明一个有希望的解决方案。这个方法应该帮助在能在设计舞台为决策被采用的选择站台(贝叶斯的网络) 产生适当风险模型。

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

A Bayesian Network Approach for Offshore Risk Analysis Through Linguistic Variables

This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.

Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network

Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.

Bayesian Inference on Type-Ⅰ Progressively Hybrid Competing Risks Model

In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.

故障树和模糊贝叶斯网络在管廊运维风险评估中的应用研究

地下综合管廊是城市的生命线,一旦出现问题就会对人们生命财产安全造成巨大损害。为了系统地分析管廊运维风险,建立了基于模糊贝叶斯网络的风险评估框架。首先,通过分析管廊运维风险源与风险形成的原因以确定风险事件和风险类别;其次,建立管廊运维风险故障树来梳理风险因素之间的逻辑关系,将故障树映射为贝叶斯网络;最后,结合专家模糊评价,构建地下综合管廊运维风险评估模型。案例分析结果显示:中间事件“火灾、爆炸”与“危害气体浓度过高”的发生概率较高。敏感性分析结果显示:“运维人员操作和维护不当”是导致管廊运维风险发生的根本事件,因此需要制定严格管理措施及规范,加强对运维人员的素质培训,以降低管廊运维过程中各种风险的发生概率。

基于动态贝叶斯网络的装配式建筑吊装施工安全风险分析

针对装配式建筑吊装施工期间风险影响因素多、不确定性高及风险状态随时间动态变化等问题,提出基于动态贝叶斯网络的装配式建筑吊装施工安全风险动态分析方法。首先,根据装配式建筑吊装施工特点和事故致因理论,从“人、物、环、管”4个方面建立风险指标体系。其次,引入时间维度建立动态贝叶斯网络模型,利用模糊理论和专家打分法量化网络节点的概率,并结合Leaky Noisy-or Gate扩展模型修正条件概率。最后,利用动态贝叶斯网络的双向推理功能对装配式建筑吊装施工安全风险进行动态风险分析。由实例分析得到某装配式建筑吊装施工安全风险的时序变化曲线,通过反向推理得到导致吊装事故发生的关键风险因素。研究成果可为分析和有效控制装配式建筑吊装施工安全风险提供新思路。

柏拉图–伽马模型下尾在险价值度量的贝叶斯估计的渐近行为及其应用

针对柏拉图–伽马风险模型的尾在险价值度量的贝叶斯估计量的渐近行为进行研究有助于对风险度量进行统计推断,以便于风险投资者及时采取相应措施规避风险。首先,通过构造柏拉图–伽马模型的贝叶斯假设,给出了尾在险价值度量的贝叶斯估计量,并利用经典的大偏差和中偏差理论,以及Delta方法得到了尾在险价值度量的贝叶斯估计量的渐近行为,包括渐近正态性、大偏差原理和中偏差原理;其次,给出了尾在险价值度量的贝叶斯估计量的中偏差原理在统计假设检验方面的具体应用,得到了第一类错误和势函数的渐近行为;最后,通过数值模拟的方法,计算并模拟了尾在险价值的置信区间及其区间覆盖率,并在不同样本容量下画出了尾在险价值度量的贝叶斯估计量标准化后的直方图和核密度估计曲线,这与标准正态分布密度函数曲线基本重合,从而验证了估计量的渐近正态性,同时还模拟了尾在险价值度量的贝叶斯估计量相关的尾概率,模拟结果表明样本容量充分大时,尾概率以一定的速度趋近于零,由此验证了估计量的中偏差原理。

暴雨灾害下的建筑施工现场风险评价研究

为了准确判断施工现场在突降暴雨情况下的安全状态,采用贝叶斯最优最劣法(Bayesian Best Worst Method,BBWM)和云模型方法,提出暴雨灾害下的建筑施工现场风险评价模型,以确定施工现场在遭受暴雨灾害时的风险等级。该模型利用了压力状态响应模型(Pressure State Response,PSR)和灾害系统理论,在考虑致灾因子危险性、孕灾环境稳定性、承灾体脆弱性和减灾能力抵御性4方面的基础上,构建18个风险因素的施工现场风险评价指标体系,并以武汉市某施工现场为例进行验证。结果显示,施工现场的减灾能力抵御性处于最重要的地位,做好现场减灾应对措施对灾害有非常重要的帮助;案例项目的评价结果处于一般风险状态,与现场实际情况相符。

基于贝叶斯攻击图的油气生产物联网系统风险评估

针对油气生产物联网系统动态风险评估问题,提出一种基于贝叶斯攻击图的油气生产物联网系统风险评估模型。首先通过对系统进行风险分析,得到入侵证据及系统漏洞,结合入侵证据和漏洞利用成功概率,采用EM算法对训练数据进行数据补全并动态更新贝叶斯攻击图的条件概率参数表,通过条件概率表可计算得出先验概率,结合入侵证据计算得到节点的后验概率,进而得到系统的风险值,考虑资源利用的相关性对风险值进行最终修正。仿真结果分析证明了该模型的有效性和准确性。

考虑人体与水流相互作用的溃坝洪水生命损失评估模型

溃坝洪水会给下游人民群众造成巨大的生命损失,开展溃坝洪水造成的生命损失风险定量评估对应急抢险救灾具有重要的现实意义。本文在已有生命损失贝叶斯网络HURAM1.0模型基础上,引入了人体稳定性物理模型,考虑人体与水流相互作用关系,对处于洪水中的人先进行稳定性判定,并进行溺水判定,运用蒙特卡洛模拟方法,综合了水深和水流速对生命损失的影响,建立HURAM2.0模型;并将该模型应用于唐家山堰塞坝溃坝洪水生命损失分析。结果表明:HURAM2.0模型建立了水流流速对生命损失影响的定量关系,更精确地刻画了人体在水流中的稳定性和求生能力,相比HURAM1.0模型对较强洪水强度条件下的生命损失预测结果更准确。同时,在本文建立的模型中,除水深度、洪水严重程度变化不大,其余变量的敏感性均有所上升,其中居民区住宅层数、在建筑物中庇护情况和溃坝时长等变量对模型计算结果的最大影响程度分别增加142%、95%和93%,加强了模型在低、中、高3类洪水强度下的解释性,与HURAM1.0相比在贝叶斯反演分析中更占优势。在唐家山堰塞坝溃坝风险分析中,HURAM2.0能区分出不同水流速条件下的生命损失,更符合实际情况,即开挖泄流槽前风险大、死亡率高,在现场勘测和开挖泄流槽后风险及死亡人数大大降低,建议结合预警疏散以降低生命损失风险。

基于数据驱动贝叶斯网络的化工事故风险分析

为减少化工厂风险分析中的主观干预,基于关联规则和贝叶斯网络构建1种数据驱动风险分析模型。该模型涵盖3个任务项,分别为数据集项、关联规则驱动项和贝叶斯网络风险评估项。首先,收集事故报告和事故因素构建事故数据库;其次,将事故数据导入Apriori算法,并根据关联规则的因素相关性确定贝叶斯网络和条件概率表结构;然后,基于事故因素出现频率计算先验概率和条件概率,并采用Fussel-Vesely计算事故因素的敏感度;最后,收集94起危险化学品中毒窒息事故实例,运用数据驱动风险分析模型评估事故因素的影响大小。研究结果可为减少和避免化工事故提供一定参考,有助于提高相关企业的整体安全水平。

基于改进DS证据理论和贝叶斯网络的海运供应链风险评估

作为一个复杂的网络组织,海运供应链为全球产业链提供运输服务,在全球化大背景下,海运供应链面临众多风险与挑战,影响供应链的安全平稳运行。因此,研究其风险至关重要。针对海运供应链风险评估体系中影响因素的不确定性和专家知识推断中存在的主观性,文章提出了基于改进DS证据理论和贝叶斯网络的海运供应链风险评估模型。结果表明,海运供应链处于较高风险等级状态,其中“信息系统风险”和“外部环境风险”概率较高,需要重点关注。该模型可以从系统的角度评估海运供应链风险,切实增强风险评估工作的科学性、准确性;评估结果可为供应链上的参与者进行风险管理提供有效参考。

远程控制船舶内河航行风险评价研究

针对远程控制船舶内河航行风险定量评价问题,提出了一种不确定性建模与船模航行试验相结合的风险评价框架,分为模型构建、量化和验证3个部分。在模型构建方面,模型节点来自内河环境、通信、岸端、船端和风险5个方面辨识的船舶远程控制航行风险因素,根据专家经验和航行试验对初始模型结构进行迭代修正,形成了远程控制船舶内河航行风险评价的贝叶斯网络模型;在模型量化方面,为量化父子节点之间线性或非线性关系提出了条件概率计算方法,在内河连续桥区开展船模航行试验,获取了部分根节点的先验概率、航行事故或险情,以及试验人员对航行风险的评价等数据;在模型验证方面,从情景分析、敏感性分析、对比分析3个角度对模型进行了验证。结果表明:在统计的40起航行试验案例中,触碰/碰撞事故占比最高,试验发现了新的失效模式,即控制冲突、程序错误、信号丢失等;构建的贝叶斯网络模型能够有效评价不同情景下航行风险水平,并辨识了与远程控制船舶内河航行风险相关的重要节点。研究结果可以为实现内河船舶远程控制作业模式提供理论依据,也为内河远程控制船舶航行安全提供研究思路。

城市地下燃气管线系统定量风险评估

随着快速的城市化进程,地下管线系统越来越复杂,地下管线系统的风险显著增加。中国地下燃气管线事故数量较多,而且其事故造成的伤亡后果远远高于其他地下管线。其他地下管线在运行过程中对地下燃气管线系统造成的风险也受到燃气领域人员的关注。为了对地下管线系统进行定性、定量和系统的风险评估,综合STAMP和BN两种系统分析方法建立STAMP-BN模型,基于STAMP模型识别系统风险和因果因素,基于STAMP结果和安全控制结构,采用BN建模进行定量风险评估。应用建立的STAMP-BN模型进行地下管线系统风险评估,结果表明造成地下燃气管线泄漏的主要中间原因是三方公司的动土作业,并揭示了第三方公司和燃气公司脆弱的根本原因。可见加强第三方公司运营层协调和燃气公司协调安排、事件应急、对其他地下管线造成的慢性危害识别方面的能力是消减风险的关键。

地下工程火灾综合定量风险评估方法

火灾是地下工程的主要灾害之一。为了全面评估地下工程火灾的风险,提出了一种基于能量和屏障理论(Energy and Barrier Theory)、预先危险性分析(Preliminary Hazard Analysis)、领结图(Bow-Tie Diagram)和模糊贝叶斯网络(Fuzzy Bayesian Network, EPB-FBN)的地下工程火灾风险评估方法。首先,基于EPB模型,分析了地下工程火灾的原因、发展、危害、产物、后果和应急救援等静态和动态信息。建立了地下工程火灾的EPB模型,并通过转换规则将EPB模型转换为BN结构。其次,利用模糊数描述变量节点的状态,建立模糊条件概率表描述节点之间的不确定逻辑关系。为了充分利用专家知识和经验数据,将概率划分为可能性区间,并使用三角模糊数表示专家判断的语言变量。采用α加权估值法进行去模糊化,得到了准确的条件概率表参数。通过模糊贝叶斯推理,可以识别关键风险因素,计算关键因素的敏感性值,并在已知证据的情况下找到最大风险链。最后,将该方法应用于情景演绎分析。结果表明:提出的EPB模型可以从能量传递、屏障失效、因果关系等角度分析地下工程火灾,揭示出地下工程火灾的风险因素及其因果关系;“心理影响”等节点是需要重点关注的因素,其最大风险传递路径可确定为火灾自动报警系统→火灾自动报警系统响应→应急救援→灭火阶段→火势全面发展阶段→热能→烧伤发热→心理影响,通过分析风险传播路径,在实际项目中通过各种措施(阻断能量传递、设置安全屏障等)切断最大风险传播路径,可以大大提高稳定性。

驾驶疲劳对危险化学品道路运输事故风险的影响规律

近年来,随着危险化学品使用量的急剧攀升,危险化学品道路运输事故率也呈现上升的趋势,且此类事故的发生往往会导致严重后果。为研究危险化学品道路运输事故动态风险变化规律,在修正贝叶斯网络模型基础上,利用2017—2021年历史数据进行机器学习,根据驾驶疲劳程度计算得到“驾驶人行为”动态节点的状态转移概率矩阵,建立基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)的危险化学品道路运输动态风险预测模型并进行推理分析。研究显示:在驾驶3 h内,驾驶人“疲劳驾驶”发生概率随时间推移而增加,但增幅有所下降;在最常见情境下,随驾驶人“疲劳驾驶”概率增加,“侧翻”和“碰撞”事故类型的发生概率明显增加,进而导致“泄漏”事故后果的发生概率有所增加;驾驶人“疲劳驾驶”概率增加会导致“有伤亡事故”发生概率增加,即加重事故的严重程度;在驾驶3 h内,“侧翻”“碰撞”“泄漏”和“有伤亡事故”发生概率的变化趋势与驾驶人“疲劳驾驶”发生概率的变化趋势一致。