水利工程高危作业人因评价体系中的认知图理论研究

为加强水利工程施工安全管理和预防事故发生,通过建立认知地图对高危作业中的人为因素进行了分析。在修订的人为因素分析与分类系统(HFACS)对高危作业人为因素进行识别的基础上,通过引入认知地图理论,建立了人为因素的因果认知地图并进行头节点、尾节点和中心性分析;提出认知广度和认知深度概念,通过数理统计各人为因素出现的频数和采用G1法求其各因素权重,建立二维平面认知地图,对事故的关键影响因素进行了识别和分析。结果表明:教育培训、作业环境和人员健康状况是导致高危作业事故最核心因素,该方法能有效对水利工程人为因素进行分析研究,具有一定适用性。     

基于HFACS的化工企业火灾爆炸事故人因分析*

为研究化工企业火灾爆炸事故的主要人为因素,以63起火灾爆炸事故案例为样本,构建人为因素分析与分类系统（HFACS）模型,进行火灾爆炸事故人为因素分类统计与分析,并利用卡方检验和比值比分析HFACS模型上下层级间的因果关系。结果表明:HFACS模型中上下层级人为因素间存在显著的因果关系,层级1中的“不良的组织氛围”及“组织过程漏洞”和层级2中“监督不充分”在HFACS模型中可以显著增大事故发生的可能性,且“资源管理漏洞”、“不良的组织氛围”、“组织过程漏洞”→“监督不充分”→“人员因素”→“违规”是引发事故的关键路径,并根据HFACS模型中引发事故的关键路径及其人为因素的主要表现形式,提出针对性的化工企业火灾爆炸事故预防措施。     

Revising HFACS for SMEs in the chemical industry: HFACS-CSMEs

Chemical accidents have occurred frequently in recent years, and most have occurred in small and medium-sized enterprises (SMEs). SMEs in the chemical industry face greater challenges than large enterprises with regard to accident prevention. However, SMEs have been unable to effectively learn from accidents due to the limited resources. The accident causation model is an effective tool to help the analyst learn from accidents. As a systematic accident causation model, the causes classification in the human factors analysis and classification system (HFACS) can match the characteristics of SMEs, but the cause of chemical accidents can be ineffectively identified by HFACS. In this study, HFACS was revised for the SMEs in the chemical industry, mainly consisting of three parts. First, based on the definition of factors in the original HFACS, the extended HFACS framework was obtained, which include 78 manifestations with the characteristics of the chemical accidents. Second, 101 accidents occurring in a SME in the chemical industry from 2012 to 2016 were analyzed though the extended HFACS framework. Finally, a new model, known as the HFACS-CSMEs, was obtained by further revising the manifestations and causes classification according to the statistical results of the accident analysis. HFACS-CSMEs consists of 15 cause factors and 56 manifestations, which can effectually identify and distinguish the causes in chemical accidents. Moreover, the easy-to-understand and statistically acceptable features of HFACS-CSMEs can cater to the SMEs regarding accident analysis. HFACS-CSMEs solves the problem that HFACS cannot be directly applied to chemical accidents and provides new ideas about preventing accidents in SMEs in the chemical industry.     

高处坠落事故的人因失误与干预策略研究

为系统分析导致高处坠落人因事故的产生机理,通过统计152起建筑工程高处坠落事故的调查与分析报告,从组织影响、安全监管、不安全行为前提条件和不安全行为等4个层次,辨识影响高处坠落事故的人为失误因素,修订人为因素分析与分类系统框架（HFACS）。设计高处坠落人因失误调查问卷,开展一线高处作业人员问卷调查,建立高处坠落人因失误结构方程模型,对导致高处坠落事故的人为失误因素进行路径分析。结果表明:各潜在因素间均呈正相关,且高处坠落人因失误事故的关键路径为资源管理不到位→安全监督培训不充分→班组管理不良→操作违规。综合各因素间相关性,提出了针对性的预防高处坠落事故的人因干预策略。     

基于贝叶斯网络的煤矿事故人因推理

煤矿事故的不可重现性决定了事故原因的调查具有很强的不确定性,如何通过事故发生后的相关信息提高事故深层次原因调查的准确性是非常重要的。将HFACS与贝叶斯网络相结合,以煤矿事故HFACS分析结果为样本,通过卡方检验和让步比分析建立人因的贝叶斯网络因果图,进一步利用最大似然估计算法确定了煤矿事故人因的贝叶斯网络参数。最后,以双柳煤业顶板事故的调查信息为证据推理导致煤矿事故发生的深层次原因,提高事故原因调查的准确性,从而验证模型的有效性。     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

建筑安全事故人为因素分类研究

为系统研究导致建筑安全事故的人为因素及对策,首先,在文献分析和专家访谈的基础上结合建筑行业特征提出人为因素分类分析系统（HFACS）框架中应增加社会环境层,在修正框架层次和人为因素的基础上构建建筑安全事故人为因素分类分析系统（C-HFACS）框架;其次,对150起建筑安全事故进行案例分析,探讨C-HFACS框架中对事故影响显著的人为因素及其内在关联性,验证了构建C-HFACS框架的合理性;最后,得出政府监管等九个人为因素对事故和下层人为因素影响显著,并从政府、企业、现场和个体四个维度提出独立第三方“飞行式”巡检等有针对性的对策,以期为建筑安全事故分析和管理提供新的方法和工具。     

基于灰色关联分析的LNG接收终端人因事故辨识方法

陆岸LNG接收终端是LNG海上进口系统中的重要设施。针对我国LNG接收终端人因事故不断增多的现状，基于航空事故调查分析中的HFACS事故致因分析模型，将人因事故的影响因素归纳为个人因素、人际因素、环境因素、监督因素和组织因素五大类，结合灰色理论中的灰色关联分析，得到了LNG接收终端人因事故辨识方法。最后，运用该方法对某LNG接收终端人因事故进行辨识，通过灰关联度的计算，找出其最主要的不安全事件来自“个人因素”中的个人“安全意识不强”。此方法克服了小样本事故数据带来的弊端，对保障LNG接收终端的安全运行具有一定的指导意义。     

基于复杂网络的内河船舶碰撞事故致因分析

为了探明内河船舶碰撞事故致因内在联系,基于船舶碰撞事故调查报告,从人-机-环-管视角构建事故致因复杂网络模型。运用“2-4”模型从人-机-环-管视角识别和提取事故致因,采用事故树方法分析调查报告中碰撞事故过程,提取内河船舶碰撞事故致因链,利用复杂网络理论融合多事故致因链,构建包括36个节点、123条边在内的事故致因网络,计算致因网络拓扑特征参数,定量分析内河船舶碰撞事故致因之间相互作用。研究结果表明：疏忽瞭望、错误估计碰撞危险、安全管理不到位、船员不适任、船与船通信信息不足、未及时行动是内河船舶碰撞事故的关键致因。同时,内河船舶碰撞致因网络是1个无标度网络,且具有小世界特性,表明事故致因之间连锁效应明显,管控上述关键致因可有效预防碰撞事故。研究结果可为预防内河船舶碰撞事故、提高内河航运管理水平提供参考。     

基于HFACS-BN模型的危险化学品事故人因路径分析*

为提高危险化学品安全管理水平,收集我国134起以人为主因的危险化学品事故,构建危险化学品事故的HFACS-BN模型,并基于贝叶斯网络对危险化学品事故中的人因路径及各因素灵敏度进行分析。研究结果表明:行为违规是导致事故的最主要不安全行为;在复杂路径中,环境因素和操作者状态具有较高的灵敏度,与其相关的风险认知与处理不当是阻碍事故预防的关键因素;在非复杂路径中,操作者状态与运行计划不当具有较高的灵敏度,其所涉及的监督违规、组织过程和组织氛围,是导致危险化学品事故的根本起点,并可依此分析事故直接原因和间接原因;操作者状态在2种路径中均表现出高灵敏度,因此化工企业在人员管理上要加强关注职工状态,减少不安全行为的出现。     

HFACS中不安全监管因素与事故致因24 Model中不安全动作因素的对比分析

为了使HFACS这种事故分析方法能够在我国各种类型事故中得到应用,研究了HFACS的不安全监管因素中各指标的定义和分类,并将其与事故致因24 Model中的不安全动作因素进行对比,得出两者的对应关系和各自特点。结果表明:HFACS中多数不安全监管因素属于24 Model中的不安全动作,这些不安全动作的发出者都是监管者;24 Model中不安全动作的发出者既可以是事故的直接引发者,也可以是监管者。从监管的范畴来看,HFACS中不安全监管因素的范围小于24 Model中的监管范围,只包括组织内部的监管。     

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.     

Coding ATC incident data using HFACS: Inter-coder consensus

Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors and the other group comprised air traffic controllers. All participants were given standardised training via a self-paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organisations and the conduct of the studies.     

Arabian,Asian, western: a cross-cultural comparison of aircraft accidents from human factor perspectives

Introduction. Rates of aviation accident differ in different regions; and national culture has been implicated as a factor. This invites a discussion about the role of national culture in aviation accidents. This study makes a cross-cultural comparison between Oman, Taiwan and the USA. Method. A cross-cultural comparison was acquired using data from three studies, including this study, by applying the Human Factors Analysis and Classification System (HFACS) framework. The Taiwan study presented 523 mishaps with 1762 occurrences of human error obtained from the Republic of China Air Force. The study from the USA carried out for commercial aviation had 119 accidents with 245 instances of human error. This study carried out in Oman had a total of 40 aircraft accidents with 129 incidences. Results. Variations were found between Oman, Taiwan and the USA at the levels of organisational influence and unsafe supervision. Seven HFACS categories showed significant differences between the three countries (p?<?0.05). Conclusion. Although not given much consideration, national culture can have an impact on aviation safety. This study revealed that national culture plays a role in aircraft accidents related to human factors that cannot be disregarded.     

Systems-based accident analysis methods: A comparison of Accimap, HFACS, and STAMP

Three accident causation models, each with their own associated approach to accident analysis, currently dominate the human factors literature. Although the models are in general agreement that accidents represent a complex, systems phenomenon, the subsequent analysis methods prescribed are very different. This paper presents a case study-based comparison of the three methods: Accimap, HFACS and STAMP. Each was used independently by separate analysts to analyse the recent Mangatepopo gorge tragedy in which six students and their teacher drowned while participating in a led gorge walking activity. The outputs were then compared and contrasted, revealing significant differences across the three methods. These differences are discussed in detail, and the implications for accident analysis are articulated. In conclusion, a modified version of the Accimap method, incorporating domain specific taxonomies of failure modes, is recommended for future accident analysis efforts.     

基于鱼骨图和层次分析法的石油静电事故影响因素分析

针对石油静电事故影响因素的复杂性、多层次性和不确定性的特点,采用鱼骨图分析法,找出了引发石油静电事故的影响因素,确定出6个主因素、25个子因素的石油静电事故评价指标体系。应用层次分析法确定了各个影响因素的重要程度,分析出导致石油静电事故的重要因素。结果表明,6个主因素中,隶属于生产操作工艺和安全生产管理的操作错误、人体静电和接地故障3个主因素权重之和高达0.792,是引发石油静电事故的主导因素;子因素中预防的重点应放在喷溅式装卸油、化纤品与人体摩擦和接地线损坏方面。鱼骨图分析法和层次分析法的联合应用,合理地解决了石油静电事故影响因素分析难题,具有一定的推广应用价值。     

自愿报告信息分析模型（CRIAM）研究

为深入分析挖掘自愿报告信息中蕴含的人为因素,在借鉴SHEL模型、REASON模型、HFACS模型、ECCAIRS模型的基础上,结合自愿报告信息内容的特点,采用上行和下行分类并行的思想构建了自愿报告信息分析模型,并运用该模型对我国航空安全自愿报告信息进行分析和对比。统计结果显示:执行不力、经验及情景意识缺乏、违规、运行计划或组织过程、压力与应激所占比重较大,对此,笔者分别从个体和组织两方面为有效实施风险管理提供了改进建议。该研究成果有助于获取重要的人为因素信息、识别出可能存在的安全隐患,为更好地改善航空安全提供了一种分析方法和思路。     

Human factors analysis of China's confined space operation accidents from 2008 to 2018

In recent years, several accidents in confined spaces have threatened the safety of staff and property in industries. A statistical analysis of 120 fatal accidents involving confined space operations in China from 2008 to 2018 was conducted in this study. The causes and characteristics of confined space operations accidents (CSOAs) were summarized. Focusing on the impact of human factors on CSOAs, the HFACS-MCS (HFACS-Modified Confined Space Accident) model, which consists of 5 levels and 21 factors, was established based on HFACS (Human Factors Analysis and Classification System). The chi-square (χ2) test and the OR analysis were implemented to analyze the statistically significant correlations between adjacent levels in the model. Finally, three human influence paths in CSOAs were derived. The results show that inadequate safety culture, organizational process vulnerability, inadequate supervision, supervisory violations, decision errors, and operational violations are the principal causes of the accidents. Besides, hurried and ad hoc rescue often caused more casualties. The targeted pre-accident prevention and post-accident emergency rescue countermeasures are proposed to prevent the recurrence of CSOAs and secondary accidents.     

基于文本挖掘和复杂网络的事故致因重要度评估方法——以房屋市政较大以上事故为例

为充分挖掘事故调查报告中的有效信息,明确安全管理工作的内容.首先,利用文本挖掘分析事故调查报告,采用最小词频阈值文档频改进信息增益评估函数对分词结果降噪,通过回溯特征项在报告中的具体表述,提取事故致因,再构建同义词词库.然后,引入复杂网络以改进TF-IDF,综合事故致因因素的关联特征评估其重要度.最后,以房屋市政较大及...     

基于贝叶斯网络模型的船舶搁浅事故分析

根据海上交通安全事故统计数据显示,船舶搁浅事故是主要的海上交通安全事故之一。为了研究船舶搁浅事故的发生机理,收集了我国东部海域部分海事局辖区的船舶搁浅事故样本。首先从人为因素、环境因素和船舶因素三方面分析导致船舶搁浅事故的原因,确定网络节点。然后找出每起搁浅事故的事故链,并建立船舶搁浅事故贝叶斯网络模型。再对建立的贝叶斯网络模型进行仿真,选取21起船舶搁浅事故对建立的模型进行验证,确定模型的有效性。最后,利用HUGIN软件找出船舶搁浅事故各影响因素的致因概率,得到导致船舶搁浅事故的致因链。在人为因素方面,瞭望不当和不熟悉航道情况占据的比例最高;在船舶因素方面,舵机设备故障对船舶搁浅事故影响最大;在环境因素方面,风/浪/流对船舶搁浅事故影响最大。