Human reliability analysis and optimization of manufacturing systems through Bayesian networks and human factors experiments: A case study in a flexible intermediate bulk container manufacturing plant

Human reliability analysis (HRA) and optimization in manufacturing systems are effective to reduce system failure. The purpose of this study is to examine the HRA and optimization through a Bayesian network (BN) model and human factors experiments (HFEs). This study was applied to a flexible intermediate bulk container manufacturing plant. The human physiological and psychological factors consisting of personal abilities of flexibility, coordination, memory, and attention were regarded as the only performance shaping factors in this study. With the BN model, the relationship between human factors and human errors was described qualitatively and the impact of the human factor on system failures was judged quantitatively. Then the workers’ abilities training with HFEs based on the fault diagnosis results was carried out. The total numbers of errors have been decreased by 69.06% and the system failure rate has been reduced significantly after training.     

Investigating the investigations: a retrospective study in the aviation maintenance error causation

In this paper, the impact of human reliability on aviation maintenance safety is investigated. Its purpose is to explore the actual mechanisms of maintenance errors initialization and propagation and hence to identify requisite characteristics for future solutions. Aviation maintenance errors account for between 12 and 15% of the global aviation accidents initiators, and this proportion rises to 23% when serious incidents are included. A wider global exercise of aviation maintenance safety improvement activities is consequently required. The current research applies the human error risk management in engineering systems (HERMES) methodology that conceptualizes two main streams of study: a retrospective investigation into human errors within aviation maintenance contexts, which is the main subject of this paper, and an ensuing prospective innovation of new tools that work to prevent future errors occurring. An extended fresh inquiry and a deeper data-mining process were concomitantly conducted. A new model signifying the initiation, accumulation, and propagation of crucial maintenance human errors within aviation maintenance organizations is thus introduced. Rotorcrafts are taken as a focal case study due to the high criticality naturally associated with their structural and operational characteristics.     

面向崩溃预测的寄存器软错误故障传播分析

故障注入是研究软错误故障传播的传统手段,但随着程序复杂性不断增加,采用故障注入对大量软错误的故障传播进行研究将花费巨大的时间成本。提出一种基于程序动态指令进行分析和建模从而快速获取软错误结果的方法。将程序转化为动态指令序列,通过体系结构正确执行分析将所有可能的软错误划分为对程序运行结果有影响和没有影响两部分;基于动态依赖图建立软错误故障传播分析模型,并建立判断程序崩溃的标准,进而提出一个算法对任意制定的能够影响程序运行结果的软错误进行故障传播分析并重点预测程序崩溃的发生。实验显示,预测的漏报率和分析单个软错误的平均用时明显低于现有方法。     

The causes of causes: determinants and background variables of human factor incidents and accidents

Abstract

1448 German Air Force pilots took part in an anonymous investigation of critical flight incidents. A 315-item questionnaire was applied, which included relevant preflight and inflight data, physiological and psychological parameters (behaviour variables, subjective state ratings before and during critical situations, pilot errors and their consequences) as well as environmental factors. Extensive data analysis revealed various human errors, which can be intepreted as a four-dimensional error-structure: vigilance errors, perception errors, information processing errors and sensorimotor errors. Determinants and background variables of human factor incidents and accidents were discussed in a model of basic man–machine system interaction patterns. Preventive measures to limit and control human factor incidents and accidents are proposed in a four-step programme.     

An investigation of a human material handler on part flow in automated manufacturing systems

This paper presents a formal approach to resolve an important question concerning changes in the control of computerized manufacturing systems when a human operator is involved as a task-performing agent. It requires building a model of human functional specifications used in executing tasks and integrating it into a control scheme for the model. More importantly, analysis of control complexity needs to be conducted to build an effective control mechanism. In this paper, a human material handler is considered, and an assessment of part flow complexity affected by human tasks in a highly automated manufacturing system is presented. For this purpose, a formal model of human task-performing processes is proposed in terms of a part and location(s) of a task. A classification for human material handling tasks is presented based on the proposed model. Furthermore, human errors and the impact of human errors on part flow are considered. Part flow complexity of a manufacturing system from the control perspective is then investigated in terms of the human tasks and errors. A shop floor control example where a human operator performs material handling tasks is provided to illustrate the proposed model.     

Errors and violations on the roads: a real distinction?

In considering the human contribution to accidents, it seems necessary to make a distinction between errors and violations; two forms of aberration which may have different psychological origins and demand different modes of remediation. The present study investigated whether this distinction was justified for self-reported driver behaviour. Five hundred and twenty drivers completed a driver behaviour questionnaire (DBQ) which asked them to judge the frequency with which they committed various types of errors and violations when driving. Three fairly robust factors were identified: violations, dangerous errors, and relatively harmless lapses, respectively. Violations declined with age, errors did not. Men of all ages reported more violations than women. Women, however, were significantly more prone to harmless lapses (or more honest) than men. These findings were consistent with the view that errors and violations are indeed mediated by different psychological mechanisms. Violations require explanation in terms of social and motivational factors, whereas errors (slips, lapses, and mistakes) may be accounted for by reference to the information-processing characteristics of the individual.     

Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies

The United States healthcare system is transitioning from paper-based to computer-based systems. In this process, it is vitally important to focus on optimizing the role of human factors in systems design. This review examines a wide range of cognitive ergonomics and socio-technical systems issues that impact the successful implementation of healthcare information technologies (HIT). We review evidence on the impact of HIT on medication errors, electronic health records and clinical support, copying clinical exams and notes, computerized-provider order entry and clinical decision support system, and bar-code medication administration systems. We examine research on barriers to successful HIT implementation and also on user workarounds of systems’ limitations. The review concludes by summarizing a series of important human factors design considerations that must be considered for successful systems implementation. This information is presented in a manner that should allow HIT system designers and implementers to readily incorporate these principles into their future developments.Relevance to industry: Evidence from a comprehensive review of the impact of HIT on a wide variety of important healthcare issues is presented and a series of human factors design considerations that are critical to successful HIT systems design is summarized.     

股票市场的高维动态因子模型及其实证分析

收益率和波动率是金融市场最重要的变量，为研究对其产生影响的因素，建立了收益率和波动率动态因子模型，并引入带惩罚的EM算法得到高维动态因子模型的稀疏参数估计。将此模型应用到沪深交所股票数据中，得到了对股票收益率和波动率产生影响的公共因子及稀疏的因子载荷矩阵。根据因子载荷矩阵，发现在两个模型中都有一个共同因子对绝大多数股票影响，其他因子是对某行业股票产生影响的行业因子。结合国内相关政策和事件等因素，分析了因子波动趋势，并给出了可能的解释。另外，利用因子贡献率，从行业角度分析了共同因子和行业因子对行业股票的影响程度。     

Development of error-compensating UI for autonomous production cells

This contribution deals with the impact of human error on the overall system reliability in flexible manufacturing systems (FMS). Autonomous production cells are used to illustrate an error-compensating system design on the basis of Sheridan's (1997) paradigm of supervisory control. In order to specify human errors and their effects in terms of system disturbances, a taxonomy of system disturbances is recommended. This taxonomic approach was derived by a value benefit analysis and is based on HEDOMS (Human Error and Disturbance Occurrence in Manufacturing Systems) with slight modifications and Reason's GEMS (Generic Error Modelling System). The taxonomy is used for data acquisition. Next, a risk priority equivalent to FMEA (Failure Mode and Effect Analysis) is introduced to structure the data according to their relevance. Then, Vicente's and Rasmussen's guidelines (1987) for an ecological interface design are related to the paradigm of supervisory control. On the basis of these guidelines four case studies are presented to show their successful applicability for interface design in FMS.     

Simulation optimization of an emergency department by modeling human errors

Emergency department (ED) is considered as one of the most critical elements in healthcare systems. This paper describes a simulation optimization of an emergency department in a general hospital in Iran by modeling human error. Human error is defined as combination of committed errors by nurses and technicians. In this study three types of errors are evaluated including repeated venipuncture, unsafe transportation and sampling errors. These errors are considered in simulation model. Seventy appropriate scenarios approved by experts in ED are defined to assess various alternatives. Scenarios are examined and evaluated by stochastic data envelopment analysis (SDEA). Then, the optimum scenarios are identified. In this study, expense cost, number of nurses and physicians are considered as inputs whilst patient duration, queue length, and number of three different committed errors are considered as outputs of SDEA and DEA model. The results show that addition of nurse and physician in ED would reduce the human errors, patient duration and queue length. Then, proposed approach is validated and verified by statistical test. Finally, queue length is identified as the most important indicator through sensitivity analysis. To the best of our knowledge this is the first paper that examines human errors in a general hospital by simulation, DEA, and SDEA approaches. Moreover, human errors are classified based on skill, rule, and knowledge (SRK) based behavior to help decision makers in order to improve the quality of care for this particular ED.     

From cognition to the system: developing a multilevel taxonomy of patient safety in general practice

The paper describes the process of developing a taxonomy of patient safety in general practice. The methodologies employed included fieldwork, task analysis and confidential reporting of patient-safety events in five West Midlands practices. Reported events were traced back to their root causes and contributing factors. The resulting taxonomy is based on a theoretical model of human cognition, includes multiple levels of classification to reflect the chain of causation and considers affective and physiological influences on performance. Events are classified at three levels. At level one, the information-processing model of cognition is used to classify errors. At level two, immediate causes are identified, internal and external to the individual. At level three, more remote causal factors are classified as either 'work organization' or 'technical' with subcategories. The properties of the taxonomy (validity, reliability, comprehensiveness) as well as its usability and acceptability remain to be tested with potential users.     

Organizing human factors knowledge for the evaluation and design of interfaces

This paper presents research conducted in the area of human factors knowledge collection and organization. Providing that a large amountof human factors engineering data is available now concerning user interface aspects, this research started from the following question: Can this knowledge be transferred to user interface designers, and by which means can this be done? An important part of human factors knowledge is included in recommendations and guidelines. It is the problems with that type of knowledge that this paper focuses on. A first attempt to tackle some of these problems is made by offering a generic model for deciphering recommendations into rules. This model stresses the importance of attributes such as human factors criteria, classes of premises, and conclusions, as well as interface objects. Definitions of criteria for organizing recommendations are offered, and a few examples of rule‐based reasoning are provided. The remaining problems with a rule approach are then discussed according to two dimensions: intrinsic problems and usage problems. In addition, it appears that an approach based on literature recommendations is not sufficient. Other areas that need further work are described, particularly concerning task and interface modelling, and human factors expertise modelling. The paper concludes on the limits and benefits of such an approach.     

基于轻量级姿态估计的跳绳检测计数算法

针对人体姿态估计算法可实施性低以及基于姿态估计的跳绳计数精度不高的问题, 提出了一种基于轻量级人体姿态估计网络的跳绳计数算法. 该算法首先输入跳绳视频, 接着利用帧间差分法提取关键帧图像并送入人体姿态估计网络进行关节点检测; 同时为了解决轻量级网络检测精度不高的问题, 提出优化的LitePose检测模型, 采用自适应感知解码方法对模型的解码部分进行优化从而减少量化误差; 然后采用卡尔曼滤波对坐标数据进行平滑降噪, 以减小坐标抖动误差; 最终通过关键点坐标变化判断跳绳计数. 实验结果表明, 在相同图像分辨率和环境配置下, 本文提出的算法使用优化的LitePose-S网络模型, 不仅未增加模型参数量和运算复杂度, 同时网络检测精度提高了0.7%, 且优于其他对比网络, 而且本算法在跳绳计数时的平均误差率最低可达1.00%, 可以利用人体姿态估计的结果有效地判断人体起跳和落地情况, 最终得出计数结果.     

基于数据挖掘的四维飞行轨迹预测模型

为了解决传统的空气动力学模型在预测四维飞行轨迹上误差较大的问题,提出一种基于数据挖掘的预测模型。该模型挖掘历史飞行时间数据,从中找出影响飞行时间的因素,预测出下一次飞行的全程时间,然后从历史位置数据中分析得出飞机在每个采样周期点上的位置,实现完整的四维轨迹预测。仿真试验验证了该模型预测的准确性和可用性。     

Modeling the potential effects of sea-level rise on the coast of New York: Integrating mechanistic accretion and stochastic uncertainty

The Sea-Level Affecting Marshes Model was applied to coastal New York State at a 5 m horizontal resolution to investigate marsh conservation and potential migration under multiple sea-level rise scenarios. Feedbacks between sea-level rise and marsh accretion rates based on mechanistic modeling were included. Simulation results predict extensive marsh losses in microtidal regimes behind the barrier islands of Long Island, vulnerable dry lands on barrier islands, and opportunities for upland migration of coastal marshes. Results also indicate changes in the composition of marsh types. Confidence of predictions due to model parameter variabilities and spatial data error were estimated with the uncertainty estimation module. Likelihood maps of land cover changes were produced. Uncertainty results suggest that variability in land cover projections is mostly due to the wide range in potential sea-level rise signals by 2100 while impact from uncertainties in model parameters, spatial data errors and linked models is less significant.     

Cognitive process modelling of controllers in en route air traffic control

In recent years, various efforts have been made in air traffic control (ATC) to maintain traffic safety and efficiency in the face of increasing air traffic demands. ATC is a complex process that depends to a large degree on human capabilities, and so understanding how controllers carry out their tasks is an important issue in the design and development of ATC systems. In particular, the human factor is considered to be a serious problem in ATC safety and has been identified as a causal factor in both major and minor incidents. There is, therefore, a need to analyse the mechanisms by which errors occur due to complex factors and to develop systems that can deal with these errors. From the cognitive process perspective, it is essential that system developers have an understanding of the more complex working processes that involve the cooperative work of multiple controllers. Distributed cognition is a methodological framework for analysing cognitive processes that span multiple actors mediated by technology. In this research, we attempt to analyse and model interactions that take place in en route ATC systems based on distributed cognition. We examine the functional problems in an ATC system from a human factors perspective, and conclude by identifying certain measures by which to address these problems. This research focuses on the analysis of air traffic controllers' tasks for en route ATC and modelling controllers' cognitive processes. PRACTITIONER SUMMARY: This research focuses on an experimental study to gain a better understanding of controllers' cognitive processes in air traffic control. We conducted ethnographic observations and then analysed the data to develop a model of controllers' cognitive process. This analysis revealed that strategic routines are applicable to decision making.     

Patient safety during medication administration: the influence of organizational and individual variables on unsafe work practices and medication errors

Medication errors are a leading cause of unintended harm to patients, both in Australia and internationally, and there is now a concerted attempt to identify and correct individual and workplace factors that encourage medication errors. The current study used structural equation modelling to measure organizational climate and to test a model with hypothesized links between climate and unsafe medication administration behaviours. The study also examined the possible mediating role of stress and morale. Data were collected from 176 nurses working in rural areas in Australia. The model provided a reasonable fit to the data with organizational climate accounting for 39% of the variance in individual distress, which in turn explained 7% of the variance in self-reported violations. The only variable that made a direct contribution to errors was violations, which accounted for 24% of the variance in medication errors. These findings highlight the importance of monitoring the state of the whole health system. Deficiencies at the organizational level affect the psychological well-being of hospital employees, and distressed employees are more likely to engage in substandard work practices that ultimately endanger the patients under their care.     

Position feedback global tracking control of EL systems: a statetransformation approach

The contribution of this paper is a dynamic position feedback global tracking controller for fully actuated Euler-Lagrange (EL) systems. The properties we show for the closed loop system are uniform stability and exponential convergence, global in the initial tracking errors and semiglobal in the initial estimation errors. The novelty of our approach is that our observer and control design are based on a new model for EL systems which is linear in the immeasurable velocities. This model is shown to fit robot manipulators. We also provide some simulation results     

An empirical study of a model for program error prediction

A model is presented for estimating the number of errors remaining in a program at the beginning of the testing phase of development. The relationships between the errors occurring in a program and the various factors that affect software development, such as programmer skill, are statistically analyzed. The model is then derived using the factors significantly identified in the analysis. On the basis of data collected during the development of large-scale software systems, it is shown that factors such as frequency of program specification change, programmer skill, and volume of program design documentation are significant and that the model based on these factors is more reliable than conventional error prediction methods based on program size alone     

Composite adaptive dynamic surface control using online recorded data

This paper presents an online recorded data‐based design of composite adaptive dynamic surface control for a class of uncertain parameter strict‐feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed‐loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time‐interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright © 2016 John Wiley & Sons, Ltd.