Digital Control Room Research Articles

Effects of secondary task eccentricity and visual salience on attention allocation in multitasking across screens

In digital control rooms, operators often need to monitor multiple information sources, which are spatially distributed on multiple screens, to manage multiple tasks simultaneously. Operators’ attention allocation between tasks are likely to be affected by the visual salience and eccentricity of secondary task screens. Their exact impacts and underlying mechanisms need to be investigated to inform system design to support balanced attention allocation strategies for optimal multitasking performance. This study aims to address these issues through a laboratory experiment using a simulated unmanned aerial vehicle monitoring platform. The participants performed a primary task and two secondary tasks concurrently, each shown on a separated screen. The visual salience and eccentricity of the maintenance task (one of the secondary tasks) were manipulated within-groups. Attention allocation behaviors were captured by eye movement data, and performance of both primary and secondary tasks were recorded. The participants were more likely to switch to and spent more time on the maintenance task of lower salience, showing a tendency to override the bottom-up influence of secondary task salience with top-down control. Large eccentricity of the secondary task, however, led to less attention allocation and lower task prioritization in paired event conflicts. The interaction effect of the salience and eccentricity of the maintenance task was significant for the accuracy of the primary task and the other secondary task. These findings offered insights into the joint design of salience and eccentricity of secondary tasks to enhance overall multitasking performance.

Real-time decision support for human–machine interaction in digital railway control rooms

This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs). In this setting, TCs decide on the spot whether to manually or automatically open signals to regulate railway traffic, a critical aspect of ensuring punctuality and safety. This time-setting specific HMI differs across TCs and is not yet supported by a data-driven tool. The proposed DSS includes agreement levels for predictions among different modeling paradigms: linear models, tree-based models, and deep neural networks. SHAP (SHapley Additive exPlanations) values are deployed to assess the agreement level in explainability between these different modeling paradigms. The prescriptions are based on the HMI of well-performing peers. We implement the DSS as proof of concept at the Belgian railway infrastructure company and report end-user feedback on the perception, the operational impact, and the inclusion of agreement levels.

Explainable real-time predictive analytics on employee workload in digital railway control rooms

Both workload peaks and lows contribute to lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention. For this purpose, we develop a real-time machine learning framework to predict and explain future workload in a challenging environment with variable and imbalanced workload: the digital control rooms for railway traffic management of Infrabel, Belgium’s railway infrastructure company. The proposed two-stage methodology leverages granular data of workload categories that are very different in nature and separates the effects of workload presence and magnitude. In this way, the set-up addresses the changing workload mix over 15-minute intervals. We extensively benchmark machine learning and deep learning models within this context, leading to LightGBM (Light Gradient Boosting Machine) as the best-performing model. SHAP (SHapley Additive exPlanations) values highlight the benefits of disentangling presence and magnitude and reveal associations with human-machine interaction and team exposure. As a proof of concept, our implemented predictive model offers tailored decision support to the traffic supervisor in an explainable way. In particular, the tool depicts overloaded and/or underloaded workstations and provides in-depth insights through local SHAP values.

Hybrid Variable Selection for Estimating Nominal HEPs and PIF Multipliers for Digital Control Rooms

With the development of new digital human-machine interfaces, many discussions in the nuclear industry have focused on the human factors issues that arise from the interfaces. To quantitatively characterize the effects of the interfaces on human reliability, we collected empirical data from a full-scope simulator of the APR1400 nuclear power plant using the Human Reliability Extraction (HuREX) framework. From the numerous variables in the collected data describing the contexts of the performance influencing factors (PIFs), including crew experience, task complexity, and procedure quality, the significant variables were identified by three techniques comprising both qualitative and quantitative analyses. Based on the selected variables, the nominal error probabilities and PIF multipliers were then estimated by logistic regression analysis. This paper interprets the meanings of the estimates and discusses the advantages of the employed variable selection techniques.

Advanced Reactor Control and Operations (ARCO): A University Research Facility for Developing Optimized Digital Control Rooms

The Advanced Reactor Control and Operations (ARCO) facility was constructed in January 2018 to serve as a test bed for advanced reactor control rooms and operator support systems. Since then, it has supported human-machine interface user experience research, fault detection and mitigation technology development, control room concept of operations development, and remote operations research. ARCO serves as the control room for the Compact Integral Effects Test (CIET) facility, which replicates the primary-side flow paths and thermal-hydraulic behavior of a fluoride-salt-cooled high-temperature reactor (FHR) using simulant fluids and scaling principles. New reactor designs feature different operating conditions and scenarios than those in existing reactors. ARCO supports the research and development of digital tools for operator communications, intuitive real-time data analysis, online health monitoring and prognostics, and control room cybersecurity. By integrating these different technologies, ARCO acts as a prototypical control system to iteratively develop methods and tools of operation in advanced small modular nuclear reactors. This paper describes the features of and challenges to operating advanced small modular reactors underlying the design basis for ARCO and its operator support systems.

Micro Task Evaluation of Digital Process Control Displays

An important aspect in evaluating modern digital control rooms is to assess if control room interfaces support efficient information acquisition by the operators. Interface efficiency is an indicator of good interface design and can therefore help in judging if conditions are right to support good operator decision making and maintenance of situation awareness. This report describes the results of a micro task data collection where 12 operators each performed 36 relatively simple control room tasks. Performance time for each task was measured. Tasks were categorized according to the type of component or system that was used, as well as if they were knowledge-based or simple identification. Results show consistently low performance times for most components. As expected, knowledge-based tasks required more time.

Designing a sustainable digital control room for public transport: a comprehensive human performance measurement model

ABSTRACT The control rooms of organizations such as bus rapid transports and automated railroad systems have changed from conventional systems with analogue equipment to modern systems with digital equipment. These changes have necessitated adaptations in the responses and habits of operators carrying out tasks. Although enhancing the system from conventional to modern is intended to improve the system performance, it often exerts the opposite effect. Therefore, the risk of decreased operator performance needs to be evaluated. This research develops a design framework incorporating comprehensive dimensions for evaluating operator performances in digital control rooms. The evaluation is based on the theory of ergonomics and is implemented by the analytical hierarchical process and fuzzy set theory. The framework has been empirically tested in digital control room for bus rapid transport and thus, it can be customized and implemented to design the work and workplace of digital control rooms for public transport design.

Develop a framework of human reliability analysis to evaluate the human error probability in DMCR of NPPs

ABSTRACT Human reliability analysis (HRA) has an important effect on the safety of nuclear power plants (NPPs). However, the operating environment in the digital main control room of NPPs has changed with the application of digital interface. Therefore, this paper developed a framework of HRA to evaluate human error probability (HEP) under the condition of digital interface control. Firstly, the hierarchical task analysis method was used to decompose the task to determine a series of sub-tasks. Secondly, the HEPs of diagnosis and execution were calculated based on the standardized plant analysis risk-human reliability analysis method. Finally, the final HEP of task was calculated by considering the recovery failure probability of human error. The experimental results showed that the HEP evaluation result of the developed method was consistent with the statistical method result. It indicated that the developed HRA method can be used to evaluate the HEP of task.

Comparisons of human reliability data between analog and digital environments

It is obvious that the management of operational risk for nuclear power plants (NPPs) is critical for their sustainability. In this regard, it should be stressed that the establishment of digital main control rooms (MCRs) in NPPs is gradually increasing these days, which are distinguished from analog MCRs equipped with conventional devices. Since the operational conditions of human operators working in digital MCRs are entirely different from those of analog MCRs, human reliability data should be clarified from the viewpoint of the raw data to be included in the related probabilistic safety assessment (PSA). In this study, two sets of human reliability data collected from both analog and digital MCRs were compared with respect to 21 basic task types that are observable during the performance of proceduralized tasks (e.g., emergency operating procedures). As a result, it seems that human error probabilities (HEPs) of the digital MCR are lower than those of the analog MCR overall. However, since certain digital MCR HEPs are higher for several particular task types, uncertainty in PSA could be enlarged if human reliability data secured from an analog MCR are directly used to conduct the PSA of a digital MCR. This paper is an extended version of the paper submitted to the ESREL 2020-PSAM15 conference.

A Fuzzy CREAM Method for Human Reliability Analysis in Digital Main Control Room of Nuclear Power Plants

Human error is an important factor leading to nuclear power plant (NPP) accidents. The increasing of the amount of information improves the operators’ human error probability (HEP) in the digital main control room of NPPs. Human reliability analysis (HRA) is considered to be an effective method to reduce human error. The Cognitive Reliability and Error Analysis Method (CREAM) is one of the widely accepted HRA methods. However, there are shortcomings that weaken the applicability of this method. Therefore, this research proposes a fuzzy CREAM method based on a combination of fuzzy logic theory and the CREAM method. The study considers the weight of common performance conditions (CPCs) and constructs a logical relationship between CPCs and control modes. Finally, the effectiveness of the proposed method is determined using a widely accepted method to validate the evaluation results. The validated results showed that the evaluation result has a consistency between the fuzzy CREAM method and the traditional CREAM method. They indicated that the fuzzy CREAM method can obtain reliable HEP.

Modeling digital main control room operator’s resilience under extreme conditions: An Experiment design scheme

Human reliability is one of the most important factors that make effects in nuclear power plant(NPP) operation. In advanced digital NPP main control room with high levels of automation, the systematic operation which require a sufficient mental workload to address those undesired events has become a critical challenge for operators. The aim of this research is to identify the operator’s reliability by developing a resilience model. In this work, a seven-stage technique framework is proposed, which includes the skeleton of theoretical analysis, experimental design and hardware setting to how to establish the model for NPP operator in a downsize main control room cabin. The resilience model for operators’ reliability via assessing their basic skill tasks performance and evaluating their cognitive workload in the framework hence can be used for assessing the level of training of the new employed operators as well as human reliability in other critical process industries.

Using Artificial Neural Network for Predicting and Evaluating Situation Awareness of Operator

The decrease of situation awareness (SA) is one of reasons leading to human factor accidents in nuclear power plants. The main purpose of this paper is to the evaluation and prediction the operators’ SA in digital main control room. Firstly, this paper used both the entropy weight method and variation coefficient method to determine the relevant influencing factors. Secondly, principal component analysis (PCA) was used to concentrate the input variables into common component. Then, an artificial neural network (ANN) model was conducted based on influencing factors and SA data. The result showed that there are identified fifteen factors that have a greater impact on SA reliability, accounting for 65.2% of the weight of all factors. The PCA result showed that the contribution rate of eight common factors reached 80.6% for the total variance of variables and the cumulative variance. Therefore, these variables were explained by eight common components. The 8-14-1 network structure was can obtain the minimum of the MSE (0.0058) and the maximum of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.9814). The predicted data can obtain the minimal MSE value (0.0035) and maximum R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.9886) when the ‘Relu’ function was used as the activation function of both the hidden layer and output layer. The average prediction accuracy of the constructed ANN model is more than exceeded 92% for the test data. This result indicated that the developed ANN model can accurately evaluate operator’s SA.

Effects of Cognitive Style and Information Acquisition Method on Diagnosis Task Performance

ABSTRACT Recently, conventional main control rooms are being gradually replaced by digital main control rooms in nuclear power plants, which results in changes of factors that have effects on diagnosis task performance. The experiment was conducted under four interfaces with different information acquisition methods (Query, Filter, Query-Filter combined, and Visual Search) to explore the influence of cognitive style on diagnosis task performance and possible interaction effects with information acquisition methods. Cognitive style was measured by Index of Learning Styles Questionnaire from four dimensions (Reflective/Active, Intuitive/Sensing, Verbal/Visual, and Global/Sequential). The results showed that cognitive style significantly affected diagnosis task performance, and had interaction effects with information acquisition method. Reflective/Active dimension had significant effect on diagnosis performance under Filter and Query-Filter combined interfaces (active participants performed better). Intuitive/Sensing dimension had significant effect on diagnosis performance under Query and Filter interfaces (intuitive participants performed better). Verbal/Visual dimension had significant effect on diagnosis performance under conventional Visual Search interface (visual participants performed better) and Query interface (verbal participants performed better). The results could be useful for human-machine interface design and the selection of operators in complex industrial systems.

Human reliability analysis for operators in the digital main control rooms of nuclear power plants

ABSTRACT As the main control room of nuclear power plants (NPPs) has been gradually digitized, new human reliability problems may emerge because of a series of new changes in the cognitive processes, behavioral patterns, and error mechanisms of operators. Aiming to address this situation, this paper proposes a method as guidance for human reliability analysis (HRA) of different cognitive Stages. This method first constructs the influencing factors of three cognitive processes, including monitoring, decision-making, and execution of actions, and then evaluates the weights of these influencing factors through an analytic hierarchy process (AHP). In this study, the parameters used in the proposed HRA method were determined by analyzing the test data obtained from a simulation model, and the results demonstrated the rationality and feasibility of the proposed method. A case example using this HRA method was given in which the human error probabilities at three stages in a nuclear power plant (NPP) steam generator tube ruptures (SGTR) accident were obtained. In summary, the proposed method is a simple and feasible HRA tool that can be applied in digital NPP main control rooms (MCRs).

The effect of communication quality on team performance in digital main control room operations

A team of operators is required for nuclear power plant operation, and communication between the operators is an important aspect of the team's ability to successfully carry out tasks. It has been difficult to evaluate the quality of this communication though, and as the relationship between communication quality and team performance has yet to be clarified, it has not been applied to most human reliability analysis (HRA) methodologies. This study investigates the relationship between the quality of communication and team performance using data from a full-scope training simulator of a digital main control room (MCR). Two important characteristics of communication were considered to determine quality: each operator's ability to self-confirm the status of a given task in a digital MCR, and the type of communication, as divided into 1-way, 2-way, and 3-way between operators. To measure team performance, the concept of an unsafe act was employed, which is defined as a human error that has the potential to negatively affect plant safety. Analysis results showed that the communication quality and team performance were related to each other. With this more clearly defined relationship, the results of this study can be applied to related performance shaping factors to improve HRA.

Empirical Study of Shared Situation Awareness between Active and Passive Group-view Displays

The purpose of this study is to develop an active group-view display and evaluate the effectiveness of the prototype to cope with team errors in terms of shared situation awareness. The results of this research provide effective countermeasures that can be used to cope with team errors from the perspective of team communication and contextual situation sharing. To develop the active group-view display, we first investigated hazardous factors that could cause team error based on the team error process; we determined countermeasures to cope with these hazardous factors, validated the countermeasures by scenario-based analysis, developed an active group-view display that adds the function of interaction among operators in nuclear facilities and, finally, validated the effectiveness of the active display in terms of team situational awareness. Based on the team error process, we determined hazardous factors for team error that may occur in the process of using the group-view display. To prevent these hazardous factors, brainstorming with experienced operators is used to establish four countermeasures and three barriers against team error. A laser writing device with the function of pointing and marking information through gestures to the group-view display is proposed. An effectiveness test of team communication and decision making using the active group-view display device is performed using SACRI. The results of the test show a significant difference between active and passive displays. This study suggests that an improved interface using an active group-view display device can be utilized as a countermeasure against team errors. In order to cope with human errors that may occur in a digital control room, it is necessary to prepare countermeasures through systematic analysis of various interfaces such as computer-based procedures, digitalized alarm indicators, and mimic-based displays in nuclear power plants.

Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants

Abstract Performance shaping factors (PSFs) in a human reliability analysis (HRA) are one that may influence human performance in a task. Most currently applicable HRA methods for nuclear power plants (NPPs) use PSFs to highlight human error contributors and to adjust basic human error probabilities (HEPs) that assume nominal conditions of NPPs. Thus far, the effects of PSFs have been treated independently. However, many studies in the fields of psychology and human factors revealed that there may be relationships between PSFs. Therefore, the inter-relationships between PSFs need to be studied to better reflect their effects on operator errors. This study investigates these inter-relationships using two data sources and also suggests a context-based approach to treat the inter-relationships between PSFs. Correlation and factor analyses are performed to investigate the relationship between PSFs. The data sources are event reports of unexpected reactor trips in Korea and an experiment conducted in a simulator featuring a digital control room. Thereafter, context-based approaches based on the result of factor analysis are suggested and the feasibility of the grouped PSFs being treated as a new factor to estimate HEPs is examined using the experimental data.

Analysis of interface management tasks in a digital main control room

Development of digital main control rooms (MCRs) has greatly changed operating environments by altering operator tasks, and thus the unique characteristics of digital MCRs should be considered in terms of human reliability analysis. Digital MCR tasks can be divided into primary tasks that directly supply control input to the plant equipment, and secondary tasks that include interface management conducted via soft controls (SCs). Operator performance regarding these secondary tasks must be evaluated since such tasks did not exist in previous analog systems. In this paper, we analyzed SC-related tasks based on simulation data, and classified the error modes of the SCs following analysis of all operational tasks. Then, we defined the factors to be considered in human reliability analysis methods regarding the SCs; such factors are mainly related to interface management and computerized operator support systems. As these support systems function to reduce the number of secondary tasks required for SC, we conducted an assessment to evaluate the efficiency of one such support system. The results of this study may facilitate the development of training programs as well as help to optimize interface design to better reflect the interface management task characteristics of digitalized MCRs.

THE HFE CONSIDERATION ON THE LIGHTING DESIGN OF MCR FOR HTR-PM IN CHINA

The main control-room is the most important monitoring and control center of nuclear power plant, and is also the most complex place in the man-machine interface of the plant. It has been thought the most frequently place where the accidents due to human factors happened. In order to avoid the possibility of error operation of operators caused by the improper lighting design, the human factors consideration for the lighting design is described in this paper according to the characteristics of digital control-room of nuclear power plant nowadays.

Transitioning Nuclear Power Plant Main Control Room From Paper Based Procedures to Computer Based Procedures

The United States (U.S.) has 99 operating Nuclear Power Plants (NPPs). The majority of these were designed and commissioned in the 1970s and 1980s. Plants are modernizing their control systems and main control rooms to be able to continue operating past their original 40-year license agreements. U.S. NPP main control rooms are migrating towards hybrid controls with both digital and analog systems. Digital upgrades, while costly, provide improved reliability, reduced maintenance cost, and the potential for fewer unplanned outages and fewer human errors. U.S. utilities have been slow to embrace computerized procedure system (CBP) research, even though CBPs demonstrate clear operational and human factors benefits. Most of the CBP research has been oriented to new reactor designs or full digital control rooms and is not applicable to the piecemeal modernization approach favored by U.S. plants. Research is needed to examine how CBPs impact operations in hybrid control rooms, and how current paper based procedures can be efficiently migrated to computerized platforms. Work is underway to develop tools and perform the obligatory research needed to design and validate CBPs for modernized U.S. nuclear control rooms.