Quantification of human error in maintenance using graph theory and matrix approach

Assessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd.     

Relationships between observed and perceived deviations from normative work procedures

Deviations from anticipated courses of events are often associated with accidents, while the effects of deviations that decrease productivity but do not obviously lead to human injury are less clear. A systemic approach to production and safety is introduced, and it is proposed that production deviations have effects that may lead to safety violations and personal injury. In addition, the relationship between observed and perceived production deviations from 12 senior (60–79 years old) males' routine work using three firewood processing machines is analysed. For simple machine work, perceived deviations were positively related to observed deviations per work cycle and inversely correlated to the perception of work efficiency. For more complex machine work it was more difficult to match observers' and operators' perceptions of deviations. Despite challenges in the production deviation concept, this approach offers a holistic understanding of the performance of human–machine-environment systems and complements assessments of deviations from safe working practice.     

Equation error versus output error methods

Abstract

For the identification of linear processes on the basis of ARX-models, equation error least squares (EELS) (often indicated as the one step ahead prediction error method) is frequently used rather than output error least squares (OELS). This is mainly because the minimum of the convex EE-criterion can easily be found, in contrast to the OE-criterion, which often displays multiple local minima. Both methods lead to the correct parameter values when the system is in the model set chosen. But in many practical situations, such as human behaviour, the real process under study will be of infinite order causing essentially different models to be found from either EE or OE criteria. Various aspects of these differences are analysed in this study. Much attention has been paid to the performance of a simulation based on a model estimated with an EELS. This simulation performance can be predicted and bounds can be given without executing the simulation itself. Furthermore the simulation performance is very poor for systems where the energy in the initial impulse response samples is very small compared with the energy in the remainder of the response. For these systems an equation error estimate cannot even provide a proper initial guess for an OELS minimization algorithm. Examples are presented that illustrate this effect.     

Visualization and quantification of water movement in porous cement-based materials by real time thermal neutron radiography:Theoretical analysis and experimental study

Water movement in porous cement-based materials is of great importance when studying their deterioration processes and durability.Many traditional methods based on mass changes,electricity or nuclear magnetic resonances are available for studying water transport in cement-based materials.In this research,an advanced technique i.e.thermal neutron radiography was utilized to achieve visualization and quantification of time dependent water movement including water penetration and moisture vapor in porous cemen...     

窃电与反窃电技术分析

分析失压和欠压窃电、TA短接分流、电压相序颠倒等错误接线和强磁干扰的窃电方式,介绍窃电情况的检查判断与电量的追补计算方法,提出使用新一代多功能电能表、推广反窃电监测报警系统等防范窃电的措施.     

保护用电流互感器饱和特性及其误差曲线研究

当电力系统发生故障时,一次电流比正常运行时的电流大几倍甚至几十倍,此时,保护用电流互感器的铁芯发生饱和,引起二次电流的畸变,从而使继电保护及其二次设备误动作。笔者对保护用电流互感器的误差与饱和特性进行了分析,介绍了实际工作中电流互感器误差曲线的绘制以及二次负载的校核方法,并提出了减小电流互感器误差的具体措施。     

测量误差理论与术语定义的发展与变化

阐述了测量误差理论的发展历程与发展方向,回顾了与误差理论和数据处理相关的国家计量技术规范跟随国际计量技术规范发展的过程,分析了国家计量技术规范中主要误差术语定义的变化情况和变化趋势。术语定义的变化表明：术语变化的趋势更具有严密的科学性、逻辑性和可操作性。     

大规模风电接入对美国电力系统运行的影响和平抑策略

至2010年底,美国的风电装机容量仅次于中国,居世界第二。考虑政府的支持政策和可观的风资源储量,有专家预见,未来美国40%的电能可来自于风力发电。在详细分析大规模风电接入对美国电力系统运行产生的影响的基础上,文中提出了需应对的几个方面的挑战。分析结果显示,最关键的挑战来自于风电功率的波动特性,可将其转化为系统负荷减去风...     

高压线路参数测试仪示值误差测量结果的不确定度评定

高压线路参数测试仪在电力系统中测量各种线路工频参数值,以作为计算系统短路电流、继电保护整定、推算潮流分布和选择合理运行方式等工作的实际依据。为了保证量值的可靠传递及满足认可实验室对检测结果的要求,文章就高压线路参数测试仪示值误差测量结果的不确定度进行了评定。     

电力市场环境下中长期负荷预测的应用分析

电力市场环境下负荷预测误差风险直接影响售电公司经营效益和供电的安全性和稳定性.为提高售电公司的经济性和稳定性,从负荷预测误差与负荷类型的研究角度出发,利用灰色系统理论、最小二乘支持向量机模型对中长期负荷进行了预测,并提出了预测加权误差这一概念,重点讨论某地区主要负荷类型与其预测加权误差之间的关联关系.分析结果使得负荷预...