Wondea Jung

A taxonomy of performance influencing factors for human reliability analysis of emergency tasks

Abstract This paper introduces the process for, and the result of, the selection of performance influencing factors (PIFs) for the use in human reliability analysis (HRA) of emergency tasks in nuclear power plants. The approach taken in this study largely consists of three steps. First, a full-set PIF system is constructed from the collection and review of existing PIF taxonomies. Secondly, PIF candidates are selected from the full-set PIF system, considering the major characteristics of emergency situations and the basic criteria of PIF for use in HRA. Finally, a set of PIFs is established by structuring representative PIFs and their detailed subitems from the candidates. As a result, a set of PIFs comprised of the 11 representative PIFs and 39 subitems was developed.

Development of the step complexity measure for emergency operating procedures using entropy concepts

Abstract For a nuclear power plant (NPP), symptom-based emergency operating procedures (EOPs) have been adopted to enhance the safety of NPPs through reduction of operators’ workload under emergency conditions. Symptom-based EOPs, however, could place a workload on operators because they have to not only identify related symptoms, but also understand the context of steps that should be carried out. Therefore, many qualitative checklists are suggested to ensure the appropriateness of steps included in EOPs. However, since these qualitative evaluations have some drawbacks, a quantitative measure that can roughly estimate the complexity of EOP steps is imperative to compensate for them. In this paper, a method to evaluate the complexity of an EOP step is developed based on entropy measures that have been used in software engineering. Based on these, step complexity (SC) measure that can evaluate SC from various viewpoints (such as the amount of information/operators’ actions included in each EOP step, and the logic structure of each EOP step) was developed. To verify the suitableness of the SC measure, estimated SC values are compared with subjective task load scores obtained from the NASA-TLX (task load index) method and step performance time obtained from a full scope simulator. From these comparisons, it was observed that estimated SC values generally agree with the NASA-TLX scores and step performance time data. Thus, it could be concluded that the developed SC measure would be considered for evaluating SC of an EOP step.

OPERA—a human performance database under simulated emergencies of nuclear power plants

Abstract In complex systems such as the nuclear and chemical industry, the importance of human performance related problems is well recognized. Thus a lot of effort has been spent on this area, and one of the main streams for unraveling human performance related problems is the execution of HRA. Unfortunately a lack of prerequisite information has been pointed out as the most critical problem in conducting HRA. From this necessity, OPERA database that can provide operators’ performance data obtained under simulated emergencies has been developed. In this study, typical operators’ performance data that are available from OPERA database are briefly explained. After that, in order to ensure the appropriateness of OPERA database, operators’ performance data from OPERA database are compared with those of other studies and real events. As a result, it is believed that operators’ performance data of OPERA database are fairly comparable to those of other studies and real events. Therefore it is meaningful to expect that OPERA database can be used as a serviceable data source for scrutinizing human performance related problems including HRA.

The step complexity measure for emergency operating procedures — comparing with simulation data

Abstract In complex systems, such as nuclear power plants (NPPs) or airplane control systems, human errors play a major role in many accidents. Therefore, to prevent occurrences of accidents or to ensure system safety, extensive effort has been made to identify significant factors that cause human errors. According to related studies, written manuals or operating procedures are revealed as one of the most important factors, and complexity or understandability of a procedure is pointed out as one of the major reasons that make procedure-related human errors. Many qualitative checklists are suggested to evaluate emergency operating procedures (EOPs) of NPPs. However, since qualitative evaluations using checklists have some drawbacks, a quantitative measure that can quantify the complexity of EOPs is imperative to compensate for them. In order to quantify the complexity of EOPs, Park et al. suggested the step complexity (SC) measure to quantify the complexity of a step included in EOPs. In this paper, to ensure the appropriateness of the SC measure, SC scores are compared with averaged step performance time data obtained from emergency training records. The total number of available records is 36, and training scenarios are the loss of coolant accident and the excess steam dump event. The number of scenario is 18 each. From these emergency training records, step performance time data for 39 steps are retrieved, and they are compared with estimated SC scores of them. In addition, several questions that are needed to clarify the appropriateness of the SC measure are also discussed. As a result, it was observed that estimated SC scores and step performance time data have a statistically meaningful correlation. Thus, it can be concluded that the SC measure can quantify the complexity of steps included in EOPs.

The MDTA-based method for assessing diagnosis failures and their risk impacts in nuclear power plants

In the emergency situations of nuclear power plants (NPPs), a diagnosis of the occurring events along an accident progression or as initiating events is crucial for managing or controlling a plant to a safe and stable condition. If the operators fail to diagnose the occurring event(s), their responses to a given event can eventually become inappropriate or inadequate. This paper presents an analytical method for assessing the potential for a diagnosis failure (or misdiagnosis) and its consequences for human behaviour and plant safety. The method largely comprises of three steps as follows: (1) Analysis of the potential for a diagnosis failure, (2) Identification of the human failure events (HFEs) that might be induced due to a diagnosis failure, and (3) Quantification of the HFEs and their modeling into a PSA model. The paper also presents a pilot application of the proposed method to the small loss of coolant accident of a Korean NPP.

A Study on the Validity of a Task Complexity Measure for Emergency Operating Procedures of Nuclear Power Plants—Comparing With a Subjective Workload

In this study, the appropriateness of the task complexity (TACOM) measure that can quantify the complexity of emergency tasks was investigated by comparing subjective workload scores with the associated TACOM scores. To this end, based on the NASA-TLX (task load index) technique, 18 operators were asked to subjectively estimate perceived workload for 23 emergency tasks that were specified in the emergency operating procedures of the reference nuclear power plants. As the result of comparisons, it was observed that subjective workload scores increase in proportion to the increase of TACOM scores. Therefore, it is expect that the TACOM measure can be used as a serviceable method to quantify the complexity of emergency tasks

A systematic approach to analysing errors of commission from diagnosis failure in accident progression

Abstract The accident scenarios of a nuclear power plant are composed of an initiating event (IE), additional events/failures and human inappropriate actions, the combinations of which lead to irreversible consequences. In such a dynamic situation, operators should diagnose the occurring events/failures (including an initiating event and additional events) and assess the related situations utilising the available resources such as operating procedures or human–machine systems to control and maintain the plant in a stable condition. The misdiagnosis or diagnosis failure of the occurring events could cause critical human inappropriate actions that aggravate the plant condition, which is termed as errors of commission (EOCs). This paper presents a methodology for analysing the potential for diagnosis failure of the initiating and additional events and the consequent EOC events, based on the operating procedures, in the accident scenarios of nuclear power plants. The method to be presented categorizes the diagnostic situations in the accident scenarios into three cases according to the structure of the emergency operating procedures (EOPs) and the time of the occurring events: (1) the diagnosis of an initiating event, (2) the diagnosis of both an initiating event and an additional event when an additional event occurs prior to the performance of the diagnosis procedure, and (3) the diagnosis of an additional event when an additional events occurs after the performance of the diagnosis procedure. The application of the method is illustrated through three case example scenarios: (1) the power-operated relief valve (PORV) or the pressurizer safety valve (PSV) LOCA, (2) the loss of all feedwater (LOAF) event (loss of main feedwater*loss of auxiliary feedwater), (3) the sequence of .

The step complexity measure for emergency operating procedures: measure verification

Abstract In complex systems, such as nuclear power plants (NPPs) or airplane control systems, human errors play a major role in many accidents. Therefore, to prevent an occurrence of accidents or to ensure system safety, extensive effort has been made to identify significant factors that can cause human errors. According to related studies, written manuals or operating procedures are revealed as one of the most important factors, and the understandability is pointed out as one of the major reasons for procedure-related human errors. Many qualitative checklists are suggested to evaluate emergency operating procedures (EOPs) of NPPs. However, since qualitative evaluations using checklists have some drawbacks, a quantitative measure that can quantify the complexity of EOPs is very necessary to compensate for them. In order to quantify the complexity of steps included in EOPs, Park et al. suggested the step complexity (SC) measure. In addition, to ascertain the appropriateness of the SC measure, averaged step performance time data obtained from emergency training records for the loss of coolant accident and the excess steam dump event were compared with estimated SC scores. Although averaged step performance time data show good correlation with estimated SC scores, conclusions for some important issues that have to be clarified to ensure the appropriateness of the SC measure were not properly drawn because of lack of backup data. In this paper, to clarify remaining issues, additional activities to verify the appropriateness of the SC measure are performed using averaged step performance time data obtained from emergency training records. The total number of available records is 36, and training scenarios are the steam generator tube rupture and the loss of all feedwater. The number of scenarios is 18 each. From these emergency training records, averaged step performance time data for 30 steps are retrieved. As the results, the SC measure shows statistically meaningful correlation with averaged step performance time data. In addition, since it is observed that the SC measure seems to have the procedure independent property (i.e. steps that have similar SC scores, whether they are included in different procedures or not, would have similar step performance time), it can be concluded that the SC measure can represent the complexity of steps included in EOPs.

Model-based identification and use of task complexity factors of human integrated systems

Task complexity is one of the conceptual constructs that are critical to explain and predict human performance in human integrated systems. A basic approach to evaluating the complexity of tasks is to identify task complexity factors and measure them. Although a great deal of task complexity factors have been studied, there is still a lack of conceptual frameworks for identifying and organizing them analytically, which can be generally used irrespective of the types of domains and tasks. This study proposes a model-based approach to identifying and using task complexity factors, which has two facets—the design aspects of a task and complexity dimensions. Three levels of design abstraction, which are functional, behavioral, and structural aspects of a task, characterize the design aspect of a task. The behavioral aspect is further classified into five cognitive processing activity types. The complexity dimensions explain a task complexity from different perspectives, which are size, variety, and order/organization. Twenty-one task complexity factors are identified by the combination of the attributes of each facet. Identification and evaluation of task complexity factors based on this model is believed to give insights for improving the design quality of tasks. This model for complexity factors can also be used as a referential framework for allocating tasks and designing information aids. The proposed approach is applied to procedure-based tasks of nuclear power plants (NPPs) as a case study to demonstrate its use. Last, we compare the proposed approach with other studies and then suggest some future research directions.

A Framework-Based Approach to Identifying and Organizing the Complexity Factors of Human-System Interaction

As the nature of human interaction with modernized socio-technical systems is increasingly cognitive and complex, many studies have been devoted to examine a range of complexity factors influencing human cognitive performance. However, there is a lack of theoretical basis of discerning and categorizing those factors. It is thus inevitable to establish a conceptual framework that can be used to identify and organize the complexity factors in an analytical way. In this paper, we regard the world of complexity factors as an abstract system and propose a new framework consisting of five views, each of which is concerned with certain aspects of the abstract system. To develop the framework more systematically, we conducted a comprehensive literature review and applied a system thinking approach to deriving a set of requirements to be satisfied by the framework. Of those five views, we particularly emphasize the roles of knowledge view. Thus a complexity factor model based on the knowledge view is also proposed. We describe two possible uses of the framework and the complexity factor model, which are the analytical identification of the complexity factors and the systematic assessment of the complexity factors identified by earlier studies.