三次指数平滑法在煤矿事故预测中的应用研究

随着我国煤炭工业的快速发展,煤矿事故频繁发生,安全生产形势依然严峻.针对煤矿事故的特点,根据我国1992-2011年煤矿事故死亡人数的统计数据,运用指数平滑法中的三次指数平滑法,建立煤矿事故预测模型,预测2012年、2013年的煤矿事故情况.结果表明,三次指数平滑法预测模型符合煤矿事故的特点,预测精度较高,适用于短期预测.由此可见,三次指数平滑法预测模型可对煤矿事故进行科学的预测与分析,为煤矿企业的安全管理提供依据,以最大限度地减少煤矿事故的发生.     

基于可拓层次分析法的铁矿危险性评价研究

我国铁矿伤亡事故后果比较严重,重特大事故时有发生。基于可拓层次分析法(EAHP),结合可拓工程方法,在物元模型理论的基础上建立了铁矿危险性评价的物元模型,聘请5位专家对铁矿危险性的评价指标体系的10项指标进行评价,利用层次分析法确定各评价指标的权重,并对大红山铁矿的危险性进行评价,得出危险性等级为危险性较小、安全性较好,也说明了该铁矿需要进一步采取安全措施,加强安全管理,提高其安全程度,从而避免事故的发生,该方法为铁矿危险性评价提供了一条新的方法和手段。     

基于灰色神经网络优化组合模型的火灾预测研究

为了提高火灾事故预测的精度,根据我国火灾事故数据样本较小,波动性较大的特点,将遗传算法优化的灰色无偏预测模型与遗传算法优化的BP神经网络模型结合起来,建立灰色神经网络优化组合模型,充分发挥无偏灰色预测模型适用于小样本的数据预测的优势与BP神经网络处理非线性问题的优点。分别采用遗传算法优化后的无偏灰色GM（1,1）模型、遗传算法优化的BP神经网络预测模型与灰色神经网络优化组合模型对我国1998-2008年的火灾事故进行拟合,并对2009-2011年的火灾事故发生数进行预测。结果表明:灰色神经网络优化组合模型的预测误差最小,精度最高,适用于火灾事故的预测。     

近年来山西省煤矿事故特点及对策措施

为掌握近年来山西省煤矿事故的发生特点,基于2015年—2018年山西省煤矿事故数据并结合全国煤矿事故数据进行对比,对事故等级、类型、地点、企业性质、煤矿辖区5个维度进行统计分析。结果表明:煤炭价格与煤矿安全形势有正负影响;近年来山西省煤矿安全总体形势好于全国,但机电、运输事故频发,水害事故时有发生,需要对该类事故加强防范措施;回采工作面为事故多发地点,这与全国煤矿事故特点不尽相同;国有重点煤矿的事故起数、死亡人数均高于地方煤矿企业,这主要是由于国有重点煤矿数量多、产量大的原因。应同时努力提高员工素质,坚决落实安全责任制,实现煤矿企业的安全生产。     

控制图分析法在煤矿安全管理中的应用

根据实践,介绍控制图分析法在煤矿安全管理中的应用,将控制图法应用于安全风险指标的统计分析可以对风险指标的发展趋势作出科学判断,评价系统安全状态是否有明显好转或恶化,检验安全管理及技术措施是否有效。以样本矿井为例,分别以月平均受伤人数、月平均计划外瓦斯超限次数为风险评价指标进行控制图分析。应用伤亡事故控制图进行安全管理,其主要优点是能够明确伤亡事故管理目标,掌握事故发展规律与趋势,有利于总结经验,吸取教训,在动态中进行安全管理。控制图分析法在煤矿安全管理中有着简便、实用、动态、预测等多种优点,对煤矿企业安全管理具有推广和借鉴意义。     

EOC方法在煤矿安全生产事故中的应用研究

基于目前各种针对煤矿事故分析方法仍停留在对事故的预测估计、管理理念、法律上,提出EOC法,EOC方法是专门用于煤矿事故分析处理及控制的方法,通过采用一种改进的层次分析法进行分析,然后对结果进行目标控制,从而达到控制事故发生次数的目的.结合煤矿冒顶事故进行案例分析,结果表明:EOC相关性分析与ANP法相结合省去了层次分析前期的复杂因素分层,更加方便简单;导人事故发生的原因数据,可以动态地观测事故致因与时间的关系,方便管理者具体问题具体分析;可以控制事故发生尽量在一个目标水平之下,而且方便安全管理的绩效考评,对煤矿安全生产事故的预防控制具有一定的指导与推广意义.     

Experts,Bayesian Belief Networks,rare events and aviation risk estimates

Bayesian Belief Networks (BBN) are conceptually sensible models for aviation risk assessment. The aim here is to examine the ability of BBN-based techniques to make accurate aviation risk predictions. BBNs consist of a framework of causal factors linked by conditional probabilities. BBN conditional probabilities are elicited from aviation experts. The issue is that experts are not being asked about their expertise but about others’ failure rates. A simple model of expertise, which incorporates the main features proposed by researchers, implies that a best-expert’s estimates of failure rates are based on accessible quantitative data on accidents, incidents, etc. Best-expert estimates will use the best available and accessible data. Depending on the frequency of occurrence, this will be data on similar events, on similar types of event, or general mental rules about event frequencies. These considerations, plus the need to be cautious about statistical fluctuations, limit the accuracy of conditional probability estimates. The BBN framework assumes what is known as the Causal Markov Condition. In the present context, this assumes that there are no hidden common causes for sequences of failure events. Examples are given from safety regulation comparisons and serious accident investigations to indicate that common causes may be frequent occurrences in aviation. This is because some States/airlines have safety cultures that do not meet ‘best practice’. BBN accuracy might be improved by using data from controlled experiments. Aviation risk assessment is now very difficult, so further work on resilience engineering could be a better way of achieving safety improvements.     

煤矿管理者教育经历与煤矿安全生产关联性分析

为探讨煤矿管理者的教育经历与煤矿生产和安全的关系,在分析事故致因理论和教育收益理论基础上,使用明瑟收益率模型,构建煤矿矿长文化程度、受教育时长、从业时长等个人教育经历与煤矿生产能力和安全水平的关系假设,并结合全国煤矿矿长调研活动收集的数据进行实证分析。结果表明:煤矿矿长的文化程度、受教育时长、从业时长与煤矿企业的生产存在正向影响关系;煤矿矿长的文化程度与煤矿的伤亡人数存在负向影响关系;并在此结果基础上提出了下一步研究方向和实践建议。     

Advanced multi-perspective computer simulation as a tool for reliable consequence analysis

Major accidents involving hazardous materials are a crucial issue for the chemical and process industries. Many accidental events taken place in the past showed that dangerous substances may pose a severe threat for people and property. Aiming at loss prevention, a series of actions have been instituted through international regulations concerning hazardous installations safety preparedness. These actions involve efficient land-use planning, safety studies execution, as well as emergency response planning drawing up. A key factor for the substantial consideration of the above is the effective prediction of possible accident forms and their consequences, for the estimation of which, a number of empirical models have been developed so far. However, (semi-)empirical models present certain deficiencies and obey to certain assumptions, thus leading to results of reduced accuracy. Another approach that could be used for this purpose and it is discussed in this work, is the utilization of advanced computational fluid dynamics (CFD) techniques in certain accident forms modeling. In particular, composite CFD-based models were developed for the simulation of several characteristic accident forms involving isothermal and non-isothermal heavy gas dispersion, confined and unconfined explosion in environment of complex geometry, as well as flammable cloud fire. The simulation cases were referred to real-scale trials allowing us to conclude about the validity of the quantitative results. Comparisons of the computational predictions with the experimental observations showed that obtained results were in good agreement with the experimental ones, whereas the evaluation of statistical performance measures proved the simulations to be statistically valid.     

Transferability of accident prediction models

Accident prediction models, the vast majority of which are negative binomial regression models, are of considerable importance to highway agencies since they can be used to conduct many traffic safety studies. However, not every agency possesses sufficient accident statistics that enable it to develop reliable models of its own. This problem gives rise to interest in the transferability of accident prediction models in time and space. It would save time, effort, and money if accident prediction models developed for one region in one period of time could be applied in different time periods and regions to produce reliable safety studies.This paper presents methods for recalibrating negative binomial accident models before transferring them for use in different time periods and regions of space. The paper emphasizes that the recalibration of the shape parameter of a transferred model using local data is absolutely necessary. It explains that it is also desirable to recalibrate the constant term of the transferred model in order to allow the model to better suit local conditions. A moment method is presented for recalibrating the shape parameter of a transferred model when its constant term is not recalibrated. However, a maximum likelihood method is presented for recalibrating both the shape parameter and the constant term of the transferred model and is shown to be superior to the recalibration methods existing in the traffic safety literature.     

基于MFOA-SVR露天矿边坡变形量预测研究

为实现边坡危险性及时预警预报,以露天矿边坡变形量为研究对象,提出采用七项影响指标作为边坡位移变形量的响应参数,建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化,构建基于MFOA-SVR露天矿边坡变形量协同预测模型,并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm,平均相对误差为4.2737%,较其他模型预测精度高,综合性能好,将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。     

2010—2019年我国特别重大事故规律研究

为研究我国现阶段特别重大事故规律特征,选取2010—2019年的34起特别重大安全事故作为研究对象,利用统计学手段,以这些事故的发生地域、时间、死亡人数、事故分类等因素作为统计标准进行分析。研究发现:从时间角度,我国特别重大事故在近10年整体呈现震荡下降趋势;每年下半年,尤其是8月份为特别重大事故的高发期;特别重大交通事故易发生在夜间与午后,煤矿事故易发生在上午至中午,爆炸事故易发生在上午。针对不同行业或事故类别,交通运输及煤矿行业为特别重大事故的高发行业,爆炸事故为特别重大事故的高发类型且事故后果最为严重。同时,发现同类事故在同地区、同年月、同时段均存在聚集性现象。     

我国矿山透水事故致因分析及安全管理对策

为找出影响矿山透水事故的主要因素,提出有针对性的管理对策.首先利用统计分析方法对80起透水事故案例原因进行统计分析,结果显示导致矿山透水事故的直接原因主要有违章、非法开采、越界开采、防水设施不符合规范要求、水文地质资料不清、破坏防水设施;间接原因主要有思想麻痹、安全监督检查不到位、违反探放水原则、法律意识淡薄、安全教育培训不到位.其次,结合透水事故的发生机理,利用鱼刺图法从人、物、环境和管理等方面进行系统分析.综合统计分析和系统分析,确定影响矿山透水事故的主要因素为管理因素.在此基础上,提出了相应的安全管理对策.     

我国工伤事故死亡率分析及其ARIMA预测

对我国工伤事故死亡率的历史数据进行分析,介绍ARIMA模型预测法。根据统计学理论,运用ARIMA模型,借助相关软件预测我国工伤事故死亡率,可知未来几年,工伤事故的千人死亡率总体上呈逐年下降的趋势;与移动平均法、指数平滑法预测结果作比较,可知ARIMA模型预测效果最佳;同时,由于影响工伤事故的因素很多,与长期预测相比,ARIMA模型更适合于对工伤事故死亡率作短期预测。     

Organizational Climate Metrics as Safety,Health and Environment Performance Indicators and an Aid to Relative Risk Ranking within Industry

The chemical, pharmaceutical and other related process industries are characterized by inherently hazardous processes and activities. To ensure that considered risk management decisions are made it is essential that organizations have the ability to rank the risk profiles of their assets and operations. Current industry risk ranking techniques are biased toward the assessment of the risk potential of the asset or operation. Methodologies used to assess these risks tend to be engineering-based and include, for example, hazard identification and event rate estimation techniques. Recent research has associated lagging safety performance indicators with metrics of organizational safety climate. Despite the evidence suggesting their potential usefulness, organizational climate metrics have not yet been exploited as a proactive safety, health and environmental performance indicator or as an aid to relative risk ranking. This paper summarizes research that successfully produced a statistical model of organizational climate and its relationship to site significant injury frequency rates, allowing the relative risk ranking of sites based upon organizational climate metrics. The responses to an industrial organizational survey are examined for a pharmaceutical company's sites in the United Kingdom, Sweden and the United States. Projection to Latent Structures Analysis is performed on the survey responses. The resultant models are shown to be able to accurately model the site significant injury frequency rates. The organizational climate metrics that discriminate between the safety performance levels of different sites are identified.     

基于GLM模糊评价在煤矿安全中的应用

煤矿安全评价是现代绿色矿山建设的主要指标之一。为对煤矿安全进行有效的评价,综合分析影响煤矿安全的人员因素、地质因素、技术设备因素、环境因素等各项指标。应用广义线性理论和SAS统计分析方法,建立基于GLM模糊评价模型。通过计算各项指标对应等级的煤矿安全影响权重〖AKω- ij,结合模糊综合评价方法得出煤矿的安全生产状况,同时通过优势比分析各指标的敏感性因子,得出防范指标危险性的有效措施。研究表明:该模型依据标准整理数据,操作性强,较准确地得出煤矿安全的状况,为煤矿的安全评价和管理决策提供依据。     

基于层次话题模型的煤矿安全事故致因要素研究

为促进安全发展,强化煤矿安全管理的科技支撑,在事故致因理论基础上,利用文本挖掘中的话题模型和创新性构建的层次致因要素话题模型,对我国2000—2015年发生的386起重特大煤矿事故调查报告进行了深入地挖掘、分析和研究。发现事故致因隐含的规律及各类事故之间的关联与共性,并进一步研究发现不同致因要素随时间的演化规律及致灾倾向,为煤矿安全管理找出重点,指导煤矿安全生产管理实践。     

Providing the numerical context for British casualty reduction targets

The first national target for reducing road accident casualties in Great Britain was set in 1987, the target year being 2000. As the year 2000 approached, preparations were made for setting a subsequent target, the target year being 2010. A major part of the preparation consisted of forecasting the number of casualties that might be expected in 2010, conditional upon predictions about how the volume of road travel by the various transport modes might change and the type of new road safety measures that the Government might introduce. The forecasts provided the numerical context for the casualty reduction target that the Government announced in March 2000.This paper summarises the statistical analyses of accident data from 1983 to 1998 upon which the forecasts were based, and compares the approach with alternatives that have been adopted elsewhere. It also describes how progress towards the target has been monitored annually, allowing the Government to judge whether additional efforts might be needed to achieve the target. This examination of the actual data from 1999 to 2007 shows how far the original forecasts have proved to be successful.The improvement of car secondary safety over the past 15 years has probably been the development that has had the most significant effect on the national casualty total. A statistical model is used to quantify this effect by analysis of accident data, and the results of the most recent analyses are included.     

Evolution of road risk disparities at small-scale level: example of Belgium

PROBLEM: Road accident outcomes are traditionally analyzed at state or road network level due to a lack of aggregated data and suitable analytical methods. The aim of this paper is to demonstrate usefulness of a simple spatiotemporal modeling of road accident outcomes at small-scale geographical level. METHOD: Small-area spatiotemporal Bayesian models commonly used in epidemiological studies reveal the existence of spatial correlation in accident data and provide a mechanism to quantify its effect. The models were run for Belgium data for the period 2000-2005. Two different scale levels and two different exposure variables were considered under Bayesian hierarchical models of annual accident and fatal injury counts. The use of the conditional autoregressive (CAR) formulation of area specific relative risk and trend terms leads to more distinctive patterns of risk and its evolution. The Pearson correlation tests for relative risk rates and temporal trends allows researchers to determine the development of risk disparities in time. RESULTS: Analysis of spatial effects allowed the identification of clusters with similar risk outcomes pointing toward spatial structure in road accident outcomes and their background mechanisms. From the analysis of temporal trends, different developments in road accident and fatality rates in the three federated regions of Belgium came into light. Increasing spatial disparities in terms of fatal injury risk and decreasing spatial disparities in terms of accident risk with time were further identified. IMPACT ON INDUSTRY: The application of a space-time model to accident and fatal injury counts at a small-scale level in Belgium allowed identification of several areas with outstandingly high accident (injury) records. This could allow more efficient redistribution of resources and more efficient road safety management in Belgium.     

Data mining of tree-based models to analyze freeway accident frequency

INTRODUCTION: Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. METHOD: This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. RESULTS: The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. IMPACT ON INDUSTRY: By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.