人因失误的机理及其可靠性研究

随着科技的发展 ,在系统安全中 ,机器设备可靠性越来越高 ,由于人本身的复杂性 ,人因失误变得愈来愈严重。本文基于人行为的原理 ,对人因失误的机理、影响人失误的因素、人行为的模型和失误模型及其可靠性的量化进行了分析 ,建立了计算人可靠性的威布尔分布模型 ,并对其参数进行了讨论 ,可用此来评价人的可靠性。     

APPLICATION OF GREY MODEL TOWARD RUNOFF FORECASTING1

ABSTRACT: A reliable forecasting model is essential in real‐time flood forecasting for reducing natural damage. Efforts to develop a real‐time forecasting model over the past two decades have been numerous. This work applies the Grey model to forecast rainfall and runoff owing to the model's relative ability to predict the future using a small amount of historical data. Such a model significantly differs from the stochastic and deterministic models developed previously. Ten historical storm events from two catchment areas in northern Taiwan are selected to calibrate and verify the model. Results in this study demonstrate that the proposed models can reasonably forecast runoff one to four hours ahead, if the Grey error prediction method is further used to update the output of the model.     

基于实船结构监测数据的异常检测及处理方法

目的 获得高质量实船结构监测数据,减少数据因外界干扰造成的异常现象,现开发一套面向真实海况的实船结构监测数据的异常处理方法。方法 将统计学中的Z-score异常检测方法与数据处理中常用的平均值计算法相结合,实现对船舶结构应力数据的异常处理。基于正常信号创建含有异常现象的验证数据,对新的异常处理方法进行精度验证。结果 相比Hampel滤波法、Smooth平滑函数等传统的信号处理方法,Z-score异常检测及平均值计算方法的异常处理精度最高,且以此为基础计算的结构信号统计值准确度较好。结论 Z-score异常检测及平均值计算方法可实现真实海况下的实船结构监测数据的异常处理,并在结构应力数据的价值挖掘上可提供有力支撑。     

飞机冲偏出跑道人为差错量化分析模型

为了分析飞机冲偏出跑道的人为差错,找到重点防控对象,提出了冲偏出跑道人为差错量化分析模型。首先应用STPA方法对飞机滑行、起飞及着陆程序进行建模,构建安全控制结构图,找出飞机冲偏出跑道的人为差错。然后将它作为BP神经网络的输入层节点,构建24-6-3的3层BP神经网络模型,从ASRS系统中抽取民航领域近5年的冲偏出跑道不安全事件报告作为数据源,根据发生阶段的不同将不安全事件报告分为滑行阶段、起飞阶段、着陆阶段3类,最后将数据源输入BP神经网络模型中进行训练,并利用测试数据检验模型的合理性,准确率为86. 7%。结果表明,特殊情况下机组人员操作不熟练、刹车系统/方向控制错误等是构成所有阶段冲偏出跑道的主要人为差错,飞机冲偏出跑道的发生阶段不同,影响因素的影响程度也不同。     

基于CREAM和贝叶斯网络的航空维修人为差错概率预测

航空维修差错不仅严重威胁着飞行安全,同时也会增加航空公司的维修成本。针对航空维修人员发生差错成因的复杂性以及历史事故数据缺乏的情况下,将人因可靠性与失误分析方法（CREAM）和贝叶斯网络（BN）相结合,提出一种改进的维修差错分析模型。根据维修任务构建相应的贝叶斯网络模型,为各子节点设置条件概率表（CPT）;基于维修基地的实际维修环境,对行为形成因子（PSFs）进行评估,得到共同绩效条件（CPCs）的水平;利用各CPC因子下各个行为功能失效模式的权重因子,对各认知活动进行失效概率的修正;将修正概率作为贝叶斯网络根节点的输入,利用推理机制,得到差错发生概率。通过案例分析和计算,验证了所述方法的可行性和有效性。     

大气扩散模型计算结果析疑

扩散模型是大气污染物浓度预测最重要的工具。然而不同模型的预测结果差距很大,应该理性看待和应用其结果,本研究旨在通过实例说明这些问题。同时指出,同一类型的环评预测应该使用同一种模型,其结果才具有可比性。而最重要的还是要通过降低排放量来减少污染影响,不必过于计较模型观测结果。     

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

一种压力容器封头非接触式扫描测量方法及误差分析

封头参数检测是确保封头质量,消除压力容器安全隐患的重要措施。传统接触式 的封头检测方法存在着诸多问题,为此,提出一种利用激光非接触式扫描进行封头参数 检测的方法。该方法利用支撑杆将激光扫描检测装置定位于封头端面,通过距离检测与 电机的旋转扫描,完成封头形状参数的检测。介绍该方法原理、操作过程,分析该方法 的误差。最后,对该方法进行实际测试验证。结果表明,该方法可以进行封头形状参数 的准确测量,误差同理论分析相符。     

污水生物处理建模水质数据误差来源分析与影响评价

为进一步提高国内基础建模数据质量,降低建模实践中的数据清洗难度,本研究以北京龙庆再生水厂生物建模过程中的实际数据问题为例,基于数理统计理论及国内污水水质特征分别构建误差来源分析方法与误差影响评价方法.通过设计补充采样与实验排查方案,成功识别数据误差来源并实现不同类型误差对数据准确性的影响量化评价.研究结果表明,污水处理厂日常的取样与化验过程中产生的随机误差不可忽视,特别是当大量随机误差存在时,对数据总体造成的波动影响无法通过数据量的积累而消除;而不同的采样位置与不当的样品保存方式则会引起水质发生变化,由此产生的系统误差将导致测量结果长期偏高/偏低.因此,在依赖人工测量数据进行建模的国内实践中,应首先对历史数据进行误差检验,再进行必要的建模补充采样,以避免补充数据质量受相同误差影响,减少非必要的人力、物力浪费.     

A framework for human error risk analysis of coal mine emergency evacuation in China

Evacuation from underground coal mine in emergency as soon as possible makes the difference between life and death. Human factors have an important impact on a successful evacuation, but literature review shows that there is a lack of consideration of human error risk during coal mine emergency evacuation in China. To address the above problems, in this paper, we established a framework for human error risk analysis of coal mine emergency evacuation, consisting of scenario and task analysis, risk assessment and risk reduction. A general evacuation procedure which is applicable for different causes is detailed through the scenario and task analysis. A new method based on expert judgment, named OGI-Model, is proposed to evaluate the reliability of human safety barrier. In this new approach, human safety barrier is divided into three sub-barriers, i.e., organization safety sub-barrier (OSSB), group safety sub-barrier (GSSB), and individual safety sub-barrier (ISSB). Each sub-barrier consists of a series of concrete measures against specific evacuation actions. An example is provided in this paper to demonstrate the use of this framework and its effectiveness.