Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis

In almost all industries, fire alarm systems play a vital role in the reducing the risks associated with fires and damages. Therefore, it is necessary to evaluate their reliability and performance in emergency situations. The present study aimed to use fault tree analysis (FTA) to determine the root causes involved in the failure of fire alarm systems, to use Fuzzy set theory and expert elicitation to determine relative probabilities, and finally, to evaluate the reliability of a fire alarm system using dynamic Bayesian networks (BNs) during a thirty-six months period. A total of 29 basic events were detected from the FT. The reliability of the fire alarm system was estimated at 0.954 according to the FT and 0.957 according to conventional BNs. The reliability of the fire alarm system after 36 months was estimated at 0.375 according to dynamic BNs. All the events involved in the failure of fire alarm systems were drawn in the fault tree diagram. The results indicate that remodeling of these systems and simultaneous construction activities are the most important factors in the failure of the fire alarm system. System reliability can also be increased to 0.965 by providing preventive and control measures to reduce the probability of critical events.     

The effectiveness of statistical testing when applied to logic systems

In this paper we demonstrate the effectiveness of statistical testing for error detection on the example of a Programmable Logic System (PLS). Statistical testing was introduced into this project as a complementary testing technique with the potential to quantify system reliability, after a variety of acceptance tests had been performed. An appropriate statistical testing algorithm was devised and implemented, which is described in detail in this paper. We compare the results of statistical testing with those of a variety of other testing methods employed on the PLS. In terms of differences detected per number of tests, statistical testing showed an outstanding effectiveness. Furthermore, it detected a problem, which was missed by all other testing techniques. This together with its potential for reliability quantification illustrates its importance for system validation as part of a risk-based safety-case.     

Collective work and resilience of complex systems

This article aims to describe the characteristics of collective working situations in complex systems – especially those in nuclear power plants – related to common forms of cooperation, in order to improve systems resilience. In addition, we will try to detail some aspects of collective working situations, emphasizing the differences between various forms of control. The similarities between work activities (multi-addressed messages, linkage to regulation, central and peripheral information) are nonetheless exposed. We conclude by proposing a contribution to support systems design, thus facilitating cooperation in teamwork activities.     

Field effectiveness evaluation of advanced driver assistance systems

Abstract

Objective: Advanced driver assistance systems (ADAS) are a class of vehicle technologies designed to increase safety by providing drivers with timely warnings and autonomously intervening to avoid hazardous situations. Though laboratory testing suggests that ADAS technologies will greatly impact crash involvement rates, real-world evidence that characterizes their effectiveness is still limited. This study evaluates and quantifies the association of ADAS technologies with the likelihood of a moderate or severe crash for new-model BMWs in the United States.

Methods: Vehicle ADAS option information for the cohort of model year 2014 and later BMW passenger vehicles sold after January 1, 2014 (n?=?1,063,503), was coded using VIN-identified options data. ADAS technologies of interest include frontal collision warning with autonomous emergency braking, lane departure warning, and blind spot detection. BMW Automated Crash Notification system data (from January 2014 to November 2017) were merged with vehicle data by VIN to identify crashed vehicles (n?=?15,507), including date, crash severity (delta V), and area of impact. Using Cox proportional hazards regression modeling, the study calculates the adjusted hazard ratio for crashing among BMW passenger vehicles with versus without ADAS technologies. The adjusted percentage reduction in moderate and severe crashes associated with ADAS is interpreted as one minus the hazard ratio.

Results: Vehicles equipped with both autonomous emergency braking and lane departure warning were 23% less likely to crash than those not equipped (hazard ratio [HR]?=?0.77; 95% confidence interval [CI], 0.73–0.81), controlling for model year, vehicle size and body type. Autonomous emergency braking and lane departure warning generally occur together, making it difficult to tease apart their individual effects. Blind spot detection was associated with a 14% reduction in crashes after controlling for the presence of autonomous emergency braking and lane departure warning (HR =0.86; 95% CI, 0.744–0.99). Differences were observed by vehicle type and crash type. The combined effect of autonomous emergency braking and lane departure warning was greater in newer model vehicles: Equipped vehicles were 13% less likely to crash (HR =0.87; 95% CI, 0.79–0.95) among 2014 model year vehicles versus 34% less likely to crash (HR =0.66; 95% CI, 0.57–0.77) among 2017 model year vehicles.

Conclusion: This robust cohort study contributes to the growing evidence on the effectiveness of ADAS technologies.     

A dynamic Bayesian networks modeling of human factors on offshore blowouts

An application of dynamic Bayesian networks for quantitative risk assessment of human factors on offshore blowouts is presented. Human error is described using human factor barrier failure (HFBF), which consists of three categories of factors, including individual factor barrier failure (IFBF), organizational factor barrier failure (OFBF) and group factor barrier failure (GFBF). The structure of human factors is illustrated using pseudo-fault tree, which is defined by incorporating the intermediate options into fault tree in order to eliminate the binary restriction. A methodology of translating pseudo-fault tree into Bayesian networks and dynamic Bayesian networks taking repair into consideration is proposed and the propagation is performed. The results show that the human factor barrier failure probability only increases within the first two weeks and rapidly reaches a stable level when the repair is considered, whereas it increases continuously when the repair action is not considered. The results of mutual information show that the important degree sequences for the three categories of human factors on HFBF are: GFBF, OFBF and IFBF. In addition, each individual human factor contributes different to the HFBF, those which contribute much should given more attention in order to improve the human reliability and prevent the potential accident occurring.     

复杂矿井通风网络解算风网有效性分析

复杂的矿井通风系统不仅对风网解算提出了更高的要求,对构建的通风网络进行风网检查也变得极其重要。首次提出了风网有效性分析的概念,通过分析改进的回路风量法解算的要求,从数据检查、风网检查和收敛性分析三个方面对通风网络图有效性进行了系统深入的分析,实现了相应的风网检查算法。通过进行强连通性分析可以使假拓扑风网更接近真实风网,通过对固定风量检查可以找出拓扑关联重复性设置的固定风量分支;通过对回路进行独立性检查和收敛性分析使得构建的通风网络尽量满足风网解算的要求,避免回路假收敛并提高解算收敛速度。     

Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network

In order to clarify the correlation between the evolution path of dust explosion accidents and emergency decision-making, and to accurately predict the disaster damage levels of various disaster bearing bodies. This paper extracts 56 key scenario elements from four aspects, namely state, answer, goal and environment, based on the analysis of typical dust explosion accident cases. At the same time, a general scenario evolution path of dust explosion accident is constructed. Using fuzzy number set theory and dynamic Bayes joint probability model, the accurate solution of scenario state probability was realized. With the help of accident cases and dynamic Bayes approach, the dust explosion consequence prediction index system and evaluation criteria were constructed, covering factors such as dust explosion intensity, casualties, direct economic losses, equipment damage, building damage, environmental damage and other factors. A polyethylene wax dust explosion accident in a city of China was used to verify the dust explosion accident scenario evolution model and consequences prediction model. The predicted results were in good agreement with the actual damage of various carriers of the accident, which indicated that the model could be used for dust explosion accident prediction and disaster loss prediction. The research results provided reference and technical support for the prediction of dust explosion accident evolution direction, emergency aid measures decision and deployment, disaster damage prediction and evaluation.     

Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework

The Bhopal disaster was a gas leak incident in India, considered the world's worst industrial disaster happened around process facilities. Nowadays the process facilities in petrochemical industries have becoming increasingly large and automatic. There are many risk factors with complex relationships among them. Unfortunately, some operators have poor access to abnormal situation management experience due to the lack of knowledge. However these interdependencies are seldom accounted for in current risk and safety analyses, which also belonged to the main factor causing Bhopal tragedy. Fault propagation behavior of process system is studied in this paper, and a dynamic Bayesian network based framework for root cause reasoning is proposed to deal with abnormal situation. It will help operators to fully understand the relationships among all the risk factors, identify the causes that lead to the abnormal situations, and consider all available safety measures to cope with the situation. Examples from a case study for process facilities are included to illustrate the effectiveness of the proposed approach. It also provides a method to help us do things better in the future and to make sure that another such terrible accident never happens again.     

基于贝叶斯网络的建筑火灾动态风险评估方法研究

为提高建筑火灾风险评估的准确性,建立1种智能化的动态风险评估方法。针对具体建筑的风险评估,以物联网技术为基础,构建智能消防监测系统,在建筑日常使用过程中通过动态风险评估,实现火灾风险要素的实时监测、数据传输,充分发挥大数据、云计算的支撑作用,将贝叶斯网络方法引入火灾风险定量评估过程,构建火灾动态风险评估模型;结合具体的应用实例,分析不确定因素对风险评估结果的影响。研究结果表明:基于贝叶斯网络的动态风险评估方法能较准确地反映建筑火灾风险的可能性,达到实时监测、动态评估的效果。     

基于动态贝叶斯网络的民航突发事件情景分析研究

航空运输重大突发事件的应急准备与处置存在问题,应急救援效果不甚理想。建立了基于灾害体、受灾体、孕灾环境以及抗灾体四要素的突发事件情景分析模型,并运用动态贝叶斯网络模型,构建了基于关键时间节点的突发事件情景演化模型。基于上述模型,以某航班降落时起落架故障的典型事件为例进行模型构建与分析,分析结果验证该模型运用于民航突发事件情景分析与评估的合理性和可行性。该模型为提高民航突发事件应急准备与处置能力提供了新的思路与方法。     

基于动态贝叶斯网络的石化装置火灾事故动态风险分析

为揭示石油炼化装置事故风险动态特性和事故情景演变路径,在对石化装置进行风险因素分析的基础上构建石化装置火灾事故故障树,基于贝叶斯网络非常规突发事故的演变过程,构建情景演变下的动态贝叶斯网络模型,在综合考虑应急措施的基础上,利用MATLAB软件和联合概率公式计算出各种事故场景的状态概率.以丙烯精馏装置火灾事故为例,结果表...     

基于贝叶斯估计的炼化装置动态告警管理方法研究

针对现有告警管理方法无法判断炼化装置运行中的过渡过程,对过渡过程不能进行准确有效地监测和管理,提出 了基于贝叶斯估计的动态告警线计算方法。通过训练历史过渡过程数据得到先验概率,自适应判断过渡过程和估计动态 告警线,解决了传统告警线的斜率不能赋值且只能依靠人为调节的问题,并可通过判断系统状态调节告警管理方式。经 常压塔原油进料流量调整和减压炉干气流量工艺调节的过渡过程现场数据验证,结果表明:与传统告警管理方法相比, 基于贝叶斯估计的动态告警管理方法的误告警总数量减少了87.34%,避免了告警洪水的发生,提高了炼化工艺运行的安 全性和可靠性。     

基于动态贝叶斯网络的高速铁路牵引变电所可靠性分析

结合GO-FLOW法的动态特性,将动态贝叶斯理论应用于高速铁路牵引变电所可靠性的分析中。首先将GO-FLOW法中的功能操作符、逻辑操作符、信号发生器、输入信号流等转换为相应的动态贝叶斯网络模块,并建立其条件概率表;然后根据牵引变电所主接线GO-FLOW图和主接线系统功能逻辑关系进行连接,得到基于GO-FLOW图的牵引变电所主接线的动态贝叶斯网络模型;最后运用动态贝叶斯算法对模型求解,得到了牵引变电所主接线的可靠性参数和可靠性变化曲线,结果表明当考虑部件随时间推移而失效的情况时更加符合实际。与其他方法相比,该方法考虑了分析对象的动态特征,减少了公式推导过程,简单清晰,便于实际应用。     

Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Human and organizational error data challenges in complex,large-scale systems

In complex, large-scale systems, event analyses are constrained by the quality of the data gathered, the maturity of the associated reporting system, and the training and background of the investigator and reporter. Such constraints place limits on the adequacy and strength of analyses conducted with the data. In this paper, we focus on the challenges of measuring performance variability in complex systems, using the lens of human and organizational error modeling. This paper begins with an overview of human and organizational error assessments, and then introduces the particular challenges of data needs in human reliability analyses. A case study of human and organizational error analysis in a complex, large-scale system, marine transportation in Puget Sound, is used to illustrate the impact of the data challenges on risk assessment processes. Suggestions for future research conclude the paper.     

Guidance to improve the effectiveness of process safety management systems in operating facilities

The Process Safety Management (PSM) systems at the operating facilities in the Oil & Gas and in Chemical manufacturing industries have matured over the years and have become, at most facilities, very robust and sophisticated. These programs are administrated by Process Safety (PS) teams at both the corporate business units and plant levels and have been effective in reducing the number and severity of PS events across the industries over the past 25 years or so. Incidents however are occurring at a regular interval and in recent times several noteworthy PS events have occurred in the United States which have brought into question the effectiveness of the PSM programs at play. These facilities have been applying their PSM programs with the expectation that the number and severity of PS events would decrease over time. The expected result has not been realized, especially in context to those facilities that have undergone the recent incidents. Current paper reviews a few publicly available PS performance reports of Oil & Gas and Chemical manufacturing industries. The authors identified a few factors at play that have led to these PS events based on their experience, literature review, and incident investigation reports. Most of the factors are intertwined with multiple PSM elements and it requires a holistic approach to address them. Each of the factors is described and the path forward is proposed to improve the effectiveness of PSM programs.     

A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks

Microbiologically influenced corrosion (MIC) is a microbial community assisted degradation of materials affecting chemical processing and oil and gas industries. MIC has been implicated in incidents involving loss of containment of hazardous hydrocarbons which have led to fires and explosions, economic and environmental impact. The interplay between abiotic environmental factors and dynamic biotic factors in MIC are poorly understood. There is a lack of mechanistic understanding of MIC and very few models are available to predict or assess MIC threat. Here we report on the development of a model to assess the susceptibility to MIC. The high-resolution model utilizes 60 independent nodes, including operational and historical failure analysis data, and is built by combining empirical relationships between the abiotic and biotic variables impacting MIC. Both static and dynamic Bayesian-network (BN) approaches were used to combine heuristic and quantitative states of variables to ultimately yield a susceptibility measure for MIC. A confidence-in-information metric was generated to reflect the amount of data used in the estimation. A susceptibility to MIC of 45%–60% was estimated by the model for ten different scenarios simulated using case-studies from literature. The susceptibility to MIC estimated by these scenarios was further interpreted in the context of these cases. This systems-based MIC model can be utilized as an independent estimator of susceptibility or can be incorporated as a sub-model within comprehensive safety threat assessment models currently utilized in industry.     

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.