关于一起过电压事故的调查分析

通过一起过电压事故的调查分析,找出了发生事故的主要原因,并提出了整改措施及应吸取的经验教训。     

基于贝叶斯网络的一种事故分析模型

贝叶斯网络被认为是人工智能研究中不确定性知识表示和推理的重要工具。当前在系统安全领域中已开始运用贝叶斯网络技术进行故障诊断分析，然而故障只是诱发事故的因素之一，无法系统的评价事故背后的隐患，对事故后果的预测也甚少涉及。笔者将贝叶斯网络作为一种事故分析手段，在事故致因理论的基础上提出了一种基于危险因素-事故-事故危害的三层贝叶斯网络拓扑模型；阐述了网络模型层次间的因果关联关系、各层次的构成、节点的描述方法以及网络模型的构建方法；最后通过一个天然气球罐的分析案例验证了该模型分析方法的可行性和有效性。     

航空事故调查中人的因素安全建议框架研究

对航空事故中人的不安全行为进行了系统分析,将失误划分为感知失误、记忆失误、决策失误、技能失误4个类别,将违章划分为习惯性违章和偶然性违章。将人的因素干预维度确定为组织管理、人/团队、技术、任务和环境。针对不安全行为制定相应的初步改进措施后,还需从措施的可行性、可接受性、经济性和有效性4个方面进行综合评估和取舍。实证研究表明,人的因素干预矩阵是制定安全建议的有效工具,该框架具备良好的适用性。     

A new approach to road accident rescue

Objective: This article develops and validates a new methodology and tool for rescue assistance in traffic accidents, with the aim of improving its efficiency and safety in the evacuation of people, reducing the number of victims in road accidents.

Method: Different tests supported by professionals and experts have been designed under different circumstances and with different categories of damaged vehicles coming from real accidents and simulated trapped victims in order to calibrate and refine the proposed methodology and tool.

Results: To validate this new approach, a tool called App_Rescue has been developed. This tool is based on the use of a computer system that allows an efficient access to the technical information of the vehicle and sanitary information of the common passengers. The time spent during rescue using the standard protocol and the proposed method was compared.

Conclusion: This rescue assistance system allows us to make vital information accessible in posttrauma care services, improving the effectiveness of interventions by the emergency services, reducing the rescue time and therefore minimizing the consequences involved and the number of victims. This could often mean saving lives. In the different simulated rescue operations, the rescue time has been reduced an average of 14%.     

一种事故隐患等级定量划分方法

事故隐患管理是安全管理中重要的内容之一,但目前隐患的分级标准尚处于主观判断阶段,无可行的分级方法。为了进一步明确事故隐患的分类方法,本文基于轨迹交叉理论,分析了事故发生发展的机理过程,将事故隐患分为直接因素导致的事故隐患和间接因素导致的事故隐患,进一步明确了事故隐患的定义。根据事故隐患的定义建立了"隐患危害程度"和"整改难易程度"两个事故隐患等级评判因素。采用事故树分析方法和LEC分析方法,分别分析两个评判因素的大小,再次运用事故树分析方法进行综合评判,建立一种事故隐患等级定量划分方法,最终达到对事故隐患定量分级的目的。     

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...     

基于事故机理和管理因素的地铁坍塌事故分析——以杭州地铁坍塌事故为实证

从技术角度分析地铁坍塌事故发生的机理,以安全事故致因层次为基础,将管理因素分为4部分,即人员因素、环境因素、材料因素和设备因素;进一步分析地铁坍塌事故发生的原因,构建地铁坍塌事故致因的鱼刺图;实证分析杭州地铁坍塌事故的致因,并构建杭州地铁坍塌事故致因的鱼刺图;在实证分析基础上,从地铁施工前与地铁施工时两时间段,针对人员因素、环境因素、材料因素和设备因素4方面管理因素,提出地铁坍塌事故的防范措施。以事故机理研究为基础,以管理因素研究为核心是研究地铁坍塌事故的必由之路,将对地铁工程的安全管理有一定的指导意义。     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.     

含硫油品储罐自燃机理及事故原因分析

通过企业现场调研并结合若干事故案例的剖析分析了含S油品储罐自燃事故的发生原因,总结了事故的主要影响因素。在建立含S油品储罐自燃的事故树（FTA）图的基础上,通过最小割集的计算与分析,探讨了事故的主要原因及发生条件,并提出了相应的预防控制措施。     

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data.     

A systems approach to extraordinarily major coal mine accidents in China from 1997 to 2011: an application of the HFACS approach

This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization.     

Overcoming the blame game to learn from major accidents: A systemic analysis of an Anhydrous Ammonia leakage accident

This article aims to demonstrate the need for changing the methods with which accidents are analyzed, if we truly wish to use what we uncover from them to learn and enrich our knowledge base of organizational management. The goal is to relinquish the broadly adopted and rather simplistic paradigm that accepts the search for human error and unsafe acts performed by workers, and produces “guilt diagnostics”. Instead, we use a systemic accident analysis methodology, based on the sociotechnical principle of understanding the real operating conditions in which accidents take place. In order to demonstrate the benefits of the theoretical framework, we compare the analyses of an Anhydrous Ammonia gas leakage accident in a fish processing plant using the traditional accident analysis model based on unsafe acts and the proposed systemic approach. The results favor the latter since it tends to be more reliable and offering useful recommendations to safety management processes, thus helping to prevent accidents, especially in complex systems.     

A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis

Layer of protection analysis (LOPA) is a widely used semi-quantitative risk assessment method. It provides a simplified and less precise method to assess the effectiveness of protection layers and the residual risk of an incident scenario. The outcome failure frequency and consequence of that residual risk are intended to be conservative by prudently selecting input data, given that design specification and component manufacturer's data are often overly optimistic. There are many influencing factors, including design deficiencies, lack of layer independence, availability, human factors, wear by testing and maintenance shortcomings, which are not quantified and are dependent on type of process and location. This makes the risk in LOPA usually overestimated. Therefore, to make decisions for a cost-effective system, different sources and types of uncertainty in the LOPA model need to be identified and quantified. In this study, a fuzzy logic and probabilistic hybrid approach was developed to determine the mean and to quantify the uncertainty of frequency of an initiating event and the probabilities of failure on demand (PFD) of independent protection layers (IPLs). It is based on the available data and expert judgment. The method was applied to a distillation system with a capacity to distill 40 tons of flammable n-hexane. The outcome risk of the new method has been proven to be more precise compared to results from the conventional LOPA approach.