现代统计信息分析技术在安全工程方面的应用——因子分析

因子分析是一种多元统计分析方法。因子分析的主要功能是寻求隐藏在观测数据背后，支配变量的公因子，以最简单的形式表达事物的本质概念，文中介绍了因子分析的计算方法。     

基于因子分析的机场运行安全评价模型

针对民用机场安全评估方法中存在的主观性和共线性问题,文章在利用人为因素分析与分类系统(HFACS)方法分析机场运行不安全事件,客观地提出安全评价指标基础上,采用因子分析方法建立综合评价因子,解决了各评价指标彼此间存在的共线性问题,并结合方差赋权法构建安全评价模型。以国内某大型机场为评价对象,将因子分析方法评价结果与模糊评价方法所得结果进行相关分析,验证了评价方法的有效性。     

草酸对Cr胁迫下李氏禾解毒性能的影响

结合因子分析方法分析了草酸对Cr胁迫下李氏禾解毒性能的影响。研究过氧化氢酶(CAT)、可溶性糖、生长指标的变化情况,并运用因子分析方法筛选出最能表征李氏禾生长特性的指标,进而较科学的研究外源草酸对Cr胁迫下李氏禾解毒性能的影响。Cr质量浓度为0、200、400 mg/kg的土壤中,适当浓度草酸对李氏禾CAT影响表现出极显著差异(P0.01);当土壤中Cr质量浓度200 mg/kg,草酸质量浓度为80、120 mg/kg时,可溶性糖浓度分别比本组对照降低了23.59%、32.65%;株高可作为关键因子的关键变量。外源草酸可以增强李氏禾对Cr的解毒性能且与草酸浓度有关,因子分析方法可以应用于李氏禾研究中。     

基于因子分析与BP网络的地铁施工安全预警研究

为了解决当前我国地铁施工过程的安全预警问题,构建因子分析与BP神经网络相结合的地铁施工安全预警模型。在分析地铁施工安全预警指标的基础上,采用SPSS因子分析法对调查数据进行降维,采用Visual Basic 6.0软件编写BP网络程序,并通过工程实际数据实现模型的训练及检测。研究结果表明,通过因子分析能使BP网络的输入数据从37个减少至7个,经因子分析降维后的收敛速度和计算精度均高于未经因子分析的神经网络,且误差均在10%以内。通过因子分析与BP神经网络相结合构建的耦合模型识别地铁施工过程中的不安全因素,进而有针对性地完善地铁施工的相关预警技术。     

基于因子分析的开采层瓦斯抽采方法选择

基于煤与瓦斯的七个基本参数,应用因子分析方法,得到了反应本煤层抽放难易程度、突出危险性及瓦斯涌出量(不考虑邻近层影响)的三个因子。依据因子得分对开采层瓦斯抽放难易程度及煤与瓦斯突出危险性进行了分类,进而对开采层瓦斯预抽方法进行判断。结果表明,采用因子分析的方法进行开采层瓦斯预抽方法的选择与现场抽放设计基本一致,并且对于传统方法无法分类的情况也给出了合理的分类判断。采用该方法可以较客观、合理的选择开采层瓦斯抽采方法。     

基于SE-FA的现役装备改进风险评价指标体系研究

现役装备改进是在原有产品基础上,通过采用各种改进措施,对产品的战术技术性能和结构进行局部改善,以达到提高或改进装备性能,延长装备使用寿命的目的.现役装备改进是一个复杂的系统工程过程,制约因素多、技术难度大,风险在所难免.为确保改进任务顺利进行,必须及时开展风险分析和评价研究,从而预先采取针对性措施加以防范.研究分析了现役装备改进的风险因子,构建了风险评价指标体系,并运用结构熵理论对指标体系的结构有序度进行了评价,提出了相对优化方案.最后,运用因子分析方法(FA)对构建的指标体系进行了因子分析,验证了指标体系的合理性.     

预测冲击地压危险性等级R型因子Fisher判别

为提高小样本预测冲击地压危险性等级精度,提出一种R型因子分析Fisher判别的预测模型。以砚石台煤矿为例,利用R型因子分析处理冲击地压危险性评价指标,提取原有指标特征信息,用少量主因子代替原有评价指标,定性分析冲击地压危险性等级;采用Fisher判别法分析R型因子分析处理结果,确定评价集与不同危险等级冲击地压的距离,并回判训练集,提高判断矩阵精确度;再根据判断矩阵预测冲击地压危险性等级。结果表明:该模型可弱化指标间的相互影响,明显提高小样本预测准确度。     

露天采矿爆破振动对民房破坏的旋转森林预测模型

针对爆破振动对民房破坏问题,应用因子分析及旋转森林理论,选取爆破振动幅值、主频率、主频率持续时间、灰缝强度、砖墙面积率、房屋高度、屋盖形式、圈梁构造柱、施工质量、场地条件为影响因素。建立基于因子分析的露天采矿爆破振动对民房破坏的旋转森林预测模型。利用现场实测32组爆破数据作为学习样本进行训练。采用回代估计方法进行检验,误判率为1/32。用另外12组爆破试验数据作为测试样本进行预测。结果表明,就爆破振动对民房破坏而言,灰缝强度、房屋高度、屋盖形式、圈梁构造柱及施工质量与其相关程度较高。用模型所得预测结果的误判率为1/6。     

基于情绪感染测量的群体性事件形成机理

近年来航班延误的现象越来越严重,各地航空运输旅客群体性事件频发,造成不同程度的经济损失,甚至扰乱社会秩序、威胁生命安全,如何有效预防群体性事件发生已成为当务之急。构建了航空运输旅客情绪评价指标体系,参考情绪感染量表设计调查问卷。通过测量民航旅客的情绪易感性水平,对调查数据进行因子分析,探究情绪敏感性的影响因素。基于因子分析结果,探究个体事件产生的原因,然后以社会燃烧理论为框架分析群体性事件的形成机理。最后,根据研究结果提出群体性事件的预防策略,包括营造愉快氛围、培养服务意识、建立信息反馈机制和构建情报系统。     

住宅建筑火灾财产损失影响因素及其实证研究

本研究目的在于通过实证研究确定影响住宅建筑火灾财产损失的关键因素.通过文献回顾,本文提出从居民、建筑物、消防抢救力量和火灾强度四个维度对研究命题展开讨论,构建了住宅建筑火灾财产损失影响因素模型.作者对84户受灾居民进行了问卷调查,并运用主成分因子分析和多元回归方法分析了有效数据,结果发现火灾强度、建筑结构、防火习惯、日常管理、消防设施和室外环境是导致住宅建筑火灾财产损失的关键因素.     

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

Introduction: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. Results: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.     

One,two or three dimensions of work engagement? Testing the factorial validity of the Utrecht Work Engagement Scale on a sample of Polish employees

Work engagement, as conceptualized by the Utrecht Work Engagement Scale (UWES), is a work-related positive state of mind that is characterized by vigour, dedication and absorption; however, it still remains unclear how many dimensions work engagement comprises. This study tested the factorial validity of 11 different UWES factorial models that are available in the literature using a confirmatory factor analysis approach on a large sample of multi-occupational Polish employees (N?=?1420). The two-factor UWES-6, comprising vigour and dedication, was found to be reliable and remained invariant across samples drawn from four different organizational positions. It also presented a better fit than the three-factor UWES-9, which is a scale used as a standard in contemporary research on work engagement. The findings suggest that the three-factor UWES-9 might not be an optimal measure of work engagement in Poland.     

核电厂HRA定性分析

人因可靠性分析(HRA)是核电厂概率安全评价(PSA)的重要组成部分,定性评价对核电厂庞大的数据进行筛选和分析,是HRA的基础和出发点.本文介绍了核电厂HRA定性分析的目的、原则、方法和程序,并以压水堆核电厂蒸汽发生器传热管破裂(SGTR)为具体实例进行说明.     

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.     

基于S变换的微地震监测中多源信号特征分析

为了提高微地震信号分析的精度,对模拟振动信号进行了试验,得到了时窗因子对ST(S-Transform)时频分辨率的影响,基于ST的良好时窗自适应性和高时频分辨率,进一步将其应用于泸沽铁矿巷道微地震监测数据的处理。结果表明:ST能准确提取信号属性用以构造特征向量,实现了矿山微地震监测中多源信号的量化分类,并为矿山微地震信号的自动识别与分析提出了新思路。     

基于因子分析与突变级数法的飞行安全状态评价研究

为合理评价航空公司的飞行安全状态,利用QAR超限事件作为原始数据,运用因子分析法确定评价指标及其重要程度。针对飞行运行的动态性,引入无需确定指标权重的突变级数法构建评价模型;利用某航空公司2015和2016年24个月的QAR数据,使用该模型进行评价,所得的评价结果与该公司的实际飞行安全状态基本相符合。结果表明:基于因子分析与突变级数法的飞行安全状态评价模型可用于实际评价工作中,且评价结果较为真实可靠。     

某极低放废物处理场地下水中Sr迁移形式热力学分析

将化学热力学平衡分析模式与地球化学条件相结合,形成Sr元素水文地球化学迁移形式热力学分析方法体系.计算出某特定场址地下水中Sr的化学形态和迁移形式.结果表明,Sr2 迁移形式占总量的99.38%,SrHCO3 形式占0.55%,SrCO03占0.019%.     

Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis

The work presented in this paper used a quantitative analysis of relevant risks through the development of fault tree analysis and risk analysis methods to aid real time risk prediction and safety evaluation of leak in a storage tank. Criticality of risk elements and their attributes can be used with real time data to predict potential failures likely to occur. As an example, a risk matrix was used to rank risk of events that could lead to a leak in a storage tank and to make decisions on risks to be allowed based on past statistical data. An intelligent system that recognizes increasing level(s) and draws awareness to the possibility of additional increase before unsafe levels are attained was used to analyse and make critical decisions. After a visual depiction of relationships between hazards and controls had been actualized, dynamic risk modelling was used to quantify the effect controls can potentially have on hazards by applying historical and real-time data into a probabilistic model. The output of a dynamic risk model is near real-time quantitative predictions of risk likelihood. Results from the risk matrix analysis method mixed with RTD and FTA were analyzed, evaluated, and compared.     

基于模糊推理的FRAM事故定量分析方法研究

为有效克服FRAM事故分析中无法进行定量分析的缺陷,提出结合模糊推理技术的Fuzzy FRAM模型。此改进模型基于FRAM识别系统运行状态;依据功能输出要素的时间/精度属性利用Matlab构建2阶模糊推理系统量化功能输出质量;根据通用性能条件(CPC)及功能输入耦合端口构建功能评价体系,针对评价体系中存在的不确定性信息融合及建模问题,采用模糊证据推理技术,通过模糊信度结构建立、数据处理、信息融合测度后获得功能的风险指数;以既有铁路危险品运输事故为例,验证方法的可行性。结果表明:Fuzzy FRAM模型的评估结果较为精确,是FRAM分析方法的有效补充。     

基于Logistic回归的燃气管网风险因素重要度分析方法研究

为确定燃气管网风险评估的关键风险因素,以A省各地市燃气管网为研究对象,基于燃气专家经验确定燃气管线风险等级,提出基于Logistic回归的燃气管网风险因素重要度分析方法。采用样本增强及随机抽样的方式,选取400个均衡样本作为管网评估数据输入,通过因子分析方法对其进行降维,得到3个公共因子并作为一级指标反向构建燃气管网风险评估指标体系;利用有序多分类Logistic回归方法,根据回归系数绝对值大小对风险因素进行重要度排序。研究结果表明：外界环境对燃气管网风险的贡献程度相对较高,管道自身因素和巡检养护次之。研究结果可为城市燃气风险防控提供理论依据和方法参考。