Human Reliability Analysis Methodology Research Articles

Phoenix–A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure

Human Reliability Analysis (HRA) has continuously evolved to adopt more advanced human error probability (HEP) quantification approaches. Despite several efforts and enhancements of HRA methods, many still present limitations. Phoenix HRA Methodology aims to overcome known issues by incorporating strong elements of current HRA good practices, leveraging lessons learned from empirical studies and existing and emerging HRA methods' features. Phoenix models performance influencing factors (PIFs) and their impact on failure modes through Bayesian Networks (BBNs). Despite increased use in HRA applications, the large amount of data BBNs may require is still challenging. Thus, to populate Phoenix BBNs, several data sources were combined, including other HRA methods, expert judgment, and operating experience. The paper discusses Phoenix's quantitative analysis process, the data sources used to estimate the BBN parameters, and the applied data aggregation method. It further provides the quantitative analysis procedure guide with the significant steps and sub-steps an HRA analyst requires to implement this Phoenix methodology phase. Furthermore, the method for mapping the data to Phoenix BBN parameters and for aggregating the data considering non-homogeneous data sources can be further used for other HRA methods that apply BBNs or other applications.

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, effective human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.

Path to modeling dynamic performance shaping factors in nuclear power plants operation – A review

AbstractThe evolution of the mechanism of human behavior formation analysis has significantly influenced the development of human reliability analysis (HRA), which aims to calculate human error probability (HEP) with performance shaping factors (PSFs). This paper reviews the typical HRA methods in different generations, the role of PSFs, and their interrelation‐ships in human risk modeling, with the background of nuclear power plants (NPPs). In a retrospective of typical HRA methods, PSF plays a fundamental role in assessing human performance during task operation. However, the subjectivity in defining and evaluating PSFs often leads to a partial representation of human behavior characteristics and human risk evolution, resulting in the neglect of PSF inter‐relationships and conservative HEP estimation. Recent studies have emphasized employing simulation platforms to simulate the task process and obtain data relevant to PSFs that can enable the exploration of the mutual effects to support the calculation of HEP more accurately. Compared to certain previous methods involving over‐simplification and inappropriate assumptions resulting in inaccurate results, current HRA methods are prone to the construction of HEP models based on objective data acquisition and dynamic calculations with process models. This shift enables a better illustration of the intricate relationships among PSFs. Reflecting on the current trend of HRA methodology, this paper proposes a possible PSF quantification based on physiological measurement providing accessible and objective data. It improves the shortcomings in data scarcity and time‐invariance of HEP calculation, thus more accurately and realistically responds to the accumulation and fluctuation of human risks throughout a task.

The application of human reliability analysis to carpal tunnel decompression.

Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

Investigating the applicability of human reliability analysis methods during early design stages of non-light-water nuclear power plants

Human interventions are an integral part of any system, whether through design, operation, maintenance, or upgrading. Moreover, although the reliance on human intervention in safety-related actions in advanced reactors (e.g., Generation IV) is expected to be reduced or completely replaced by automated actions, nuclear power plants (NPP) require human actions throughout their lifecycle from design, construction, operation, and decommissioning. Hence, the impacts of all operator actions are required to be captured and incorporated in the probabilistic risk assessment (PRA) of the modeled plant. Furthermore, a risk-informed and performance-based design and licensing approach expects that a PRA model, including human reliability analysis (HRA), is developed starting from the early design stages and used to inform all design iterations. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in the final design stages, though adequate in capturing pre-, at-, and post-initiators, comes short of informing the design itself in the iterative design lifecycle. Moreover, due to most HRA methodologies being developed utilizing mostly the operating experience of existing light water reactors, limited guidance is available to the applicability of different HRA methodologies during the early stages of the design. Limited guidance is available, as well, on how to utilize the results of HRA in informing the design of later iterations. Hence, this study presents an investigation of the applicability of different HRA methodologies during the early stages of the design. The structure of a representative set of nine HRA methodologies is assessed against the available operating and emergency procedures within different design stages. Furthermore, these different HRA methodologies are assessed based on the availability of guidance on how to use their results to risk-inform the design in an iterative design process. Finally, the applicability of Open Preliminary Human Importance (OpenPHI) methodology, introduced specifically to implement HRA during early design stages, is assessed during the early stages of the design and compared against other methodologies.

Bayesian belief network-based human reliability analysis methodology for start-up and shutdown operations in nuclear power plants

Human-induced unplanned reactor trips during start-up and shutdown operations can impair the safety condition of the plant or threaten important plant safety functions. Despite the need for efforts to reduce such human errors through human reliability analysis (HRA), most existing HRA focuses on emergency or full-power operations. To address this, the current study proposes an HRA methodology for start-up and shutdown operations in which a quantitative evaluation is performed using a Bayesian belief network (BBN) model based on a developed quantification framework for the start-up and shutdown operation tasks. Before the evaluation, general operating procedure (GOP) analysis, simulation-based screening analysis, and task analysis are performed. To demonstrate the process of proposed methodology and its effectiveness, a case study with a GOP is conducted. Then to verify the feasibility of the proposed methodology, two comparison cases are conducted with BBN model evaluation and actual events that occurred in Korean NPPs.

Integrating Human Barriers in Human Reliability Analysis: A New Model for the Energy Sector.

Human reliability analysis (HRA) is a major concern for organizations. While various tools, methods, and instruments have been developed by the scientific community to assess human error probability, few of them actually consider human factors impact in their analysis. The active role that workers have in shaping their own performance should be taken into account in order to understand the causal factors that may lead to errors while performing a task and identifying which human factors may prevent errors from occurring. In line with this purpose, the aim of this study is to present a new methodology for the assessment of human reliability. The proposed model relies on well-known HRA methodologies (such as SPAR-H and HEART) and integrates them in a unified framework in which human factors assume the role of safety barriers against human error. A test case of the new method was carried out in a logistics hub of an energy company. Our results indicate that human factors play a significant role in preventing workers from making errors while performing tasks by reducing human error probability. The limits and implications of the study are discussed.

Bayesian Belief Network-Based Human Reliability Analysis Methodology for Start-Up and Shutdown Operations in Nuclear Power Plants

Bayesian Belief Network-Based Human Reliability Analysis Methodology for Start-Up and Shutdown Operations in Nuclear Power Plants

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

Abstract This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.

A human reliability analysis methodology based on an extended Phoenix method for severe accidents in nuclear power plants: Qualitative analysis framework

The mitigation of the Fukushima accident exposed the detrimental action of human errors once again. However, deficiencies in existing human reliability analysis (HRA) methods prevent the effective prediction and analysis of potential human errors during the mitigation of severe accidents following core damage. To address this problem, this study presents a qualitative analysis framework for emergency response personnel (ERP) reliability analysis under severe accident conditions. This framework is developed using the Phoenix HRA method in conjunction with analyzing the ERP response characteristics and human-related issues from previous accidents, literature surveys, drill observations and interviews, as well as an assessment of the applicability of existing HRA methods. The analysis framework includes the establishment of an ERP response tree and a human failure fault tree (FT), as well as the identification and compilation of ERP cognitive functions, human error modes (HEMs), performance-influencing factors (PIFs), and the construction of Bayesian belief networks, to provide methodological guidance for the prevention of human errors under severe accident conditions.

Reliability engineering applications in electronic, software, nuclear and aerospace industries: A 20 year review (2000–2020)

A review on reliability engineering applications in 4 industrial domains namely electronic, software, nuclear and aerospace from the 2000′s to the present day is compiled. The progress in industrial maintenance activities and Human Reliability Analysis (HRA) linked to these domains are explored. Then, the mathematical aspect of reliability evaluation, in particular multi-state system (MSS), which characterizes the system complexity in these domains are presented. Trends progression are obtained through literature of respective areas as well as available technical information and industrial circulations. Through this review, trend similarities between the mentioned domains and the challenges in reliability science implementation are uncovered. The methods employed in respective industry are explained, with each strength and weakness analysed, together with application examples. This work reveals the role of Prognostic and Health Management (PHM), HRA and analytical methodologies such as MSS and Probabilistic Risk Assessment (PRA) in managing reliability of complex industrial systems. Finally, it stresses on the importance of synergy between these frameworks to ensure a complete reliability assessment in the industry.

An accident causation model based on safety information cognition and its application

Accident causation models can provide a framework to explain how an accident occurs from various perspectives. In the past, human error has become the main cause of accidents, which is inextricably linked with safety information cognition (SIC). In this study, an accident causation model—SIC-based Accident-causing Model (SICAM) is developed and then a new human reliability analysis methodology—SIC-based human factor analysis (SICHFA) is provided. Three steps are followed to construct the model. First, the role of SIC in the safety information flow (SIF) has been confirmed. Secondly, the mechanism of accident causation based on SIC were addressed, the effect of negative signal noise mainly involved. Thirdly, the influence objects of signal noise on the SIC process (SICP) were classified at information home, cognitive environment, and cognitive object. To demonstrate the viability of SICAM & SICHFA, the electrical fire, which occurred frequently and resulted in serious adverse social impact in recent years, was selected as the case in this study. Results showed that the proposed accident causation model and SICHFA can provide a new approach for preventing & predicting human accidents.

The application of human reliability analysis to three critical care procedures

BackgroundProcedures carried out in the intensive care unit are prone to human error. Standardisation has been suggested as an approach for reducing errors. This study used human reliability analysis methodologies to examine commonly performed critical care procedures: endotracheal suctioning; ultrasound-guided right internal jugular vein cannulation; and rapid-sequence intubation. MethodsThe subgoals, or individual steps, required to complete the three procedures were identified using hierarchical task analysis. The systematic human error reduction and prediction approach was then used to identify potential human errors at each subgoal, the level of risk and how these potential errors could be prevented. ResultsEndotracheal suctioning procedure was broken down into 129 subgoals, of which 49 (38.0%) were high-risk. Ultrasound-guided right internal jugular venous cannulation was divided into 224 subgoals, of which 131 (58.4%) were medium-risk, and 20 (8.9%) were identified as high-risk. Rapid sequence intubation was divided into 167 subgoals. A total of 73 (43.7%) of these subgoals were judged to be high-risk. ConclusionsThe use of human reliability analysis techniques can support healthcare professionals to gain an in-depth understanding of how particular procedures are carried out in order to reduce the risk of, and improve training in, how to perform these procedures.

A human reliability analysis methodology for oil refineries and petrochemical plants operation: Phoenix-PRO qualitative framework

The oil industry has grown in terms of quantity of facilities and process complexity. However, human and material losses still occur due to major accidents, and many of which involve human failures. These failures can be identified, modeled and quantified through Human Reliability Analysis (HRA). The most advanced HRA methods have been developed and applied in nuclear power plants, while the petroleum industry has mainly focused on process safety in terms of technical aspects of the operation and equipment. The existing HRA methodologies may not reflect the idiosyncrasies of refining and petrochemical plants regarding the interaction of the operators with the plant, their failure modes, and the factors that influence them. This paper builds on Phoenix HRA Methodology to develop a methodology specific for Petroleum Refining Operations (Phoenix-PRO). It uses as basis the Hybrid Causal Logic model, with Event Sequence Diagrams, Fault Trees and Bayesian Belief Networks. Phoenix-PRO development relied on interviews with HRA specialists, visitations to a refinery and its control room, and analysis of past oil refineries accidents. The use of this methodology for HRA of oil refineries and petrochemical plants operations can enhance this industry safety and allow for solid risk-based decisions.

Assessment of human factor in critical infrastructures

Methodology for assessing human error probability under emergency conditions, taking into account internal and external factors, the degree of uncertainty, which can influence the reliability of a “man-machine” system, is presented. A model based on the HRA (Human reliability analysis) methodology is used. Probability of a human error is determined, which combines the nominal error and the influence of environmental conditions. The error is compared to the rated (design) permissible critical value dependent on the system state. It enables steadily monitoring the technical state of the system. In case of an unsatisfactory state, correcting actions are performed and probability for a human error is again assessed as well as the extent of the system improvement is tested. The methodology uses reliability criteria taking into account the degree of human involvement into the process of controlling critical infrastructures. The methodology includes efficiency analysis and assessment of the safeguarding system with regard to the human factor. Currently, there is no special documentation setting the procedure of human behavior under emergency conditions by means of quantitative methods. The methodology can serve the purpose of developing a practical manual for people having to make decisions; it can be used in training the personnel for acting in emergency situations. It is useful for assessing the influence of human factor on operation of critical infrastructures used for various purposes.

Human Error Assessment and Reduction Technique for Reducing the Number of Marine Accidents in Indonesia

Human factors is playing an important role in every accident, particularly marine accidents. Hence, a lot of researches were conducted to analyze the human factors involvement in the accidents. Since the development of marine industry shows progressively increasing nowadays, especially in Indonesia, as Indonesia vision to be a global maritime axis of the world for marine industry, the awareness of safety life at sea has to be increase as well. Human reliability analysis (HRA) consist of many methodologies to analyze the accidents, the basic steps of HRA is qualitative method and quantitative method, one of the HRA methodology is Human Error Assessment and Reduction Technique (HEART) methodology which has been established in 1982 to assess nuclear power plant. HEART methodology is applied in this study to analyze the cause of marine accidents by human factors. The aims of this study are to know the main cause of accidents by human factors, to increase the awareness of safety at sea especially, and furthermore to improve the quality of ergonomics at sea. There are 93 EPC which discovered in this study for analyzing marine accidents in Indonesia.

Phoenix – A model-based Human Reliability Analysis methodology: Qualitative Analysis Procedure

Phoenix method is an attempt to address various issues in the field of Human Reliability Analysis (HRA). Built on a cognitive human response model, Phoenix incorporates strong elements of current HRA good practices, leverages lessons learned from empirical studies, and takes advantage of the best features of existing and emerging HRA methods. Its original framework was introduced in previous publications. This paper reports on the completed methodology, summarizing the steps and techniques of its qualitative analysis phase. The methodology introduces the “Crew Response Tree” which provides a structure for capturing the context associated with Human Failure Events (HFEs), including errors of omission and commission. It also uses a team-centered version of the Information, Decision and Action cognitive model and “macro-cognitive” abstractions of crew behavior, as well as relevant findings from cognitive psychology literature and operating experience, to identify potential causes of failures and influencing factors during procedure-driven and knowledge-supported crew-plant interactions. The result is the set of identified HFEs and likely scenarios leading to each. The methodology itself is generic in the sense that it is compatible with various quantification methods, and can be adapted for use across different environments including nuclear, oil and gas, aerospace, aviation, and healthcare.

Human reliability assessment during offshore emergency conditions

This paper presents a quantitative approach to Human Reliability Analysis (HRA) during emergency conditions in an offshore environment. Due to the lack of human error data for emergency conditions most of the available HRA methodologies are based on expert judgment techniques. Expert judgment suffers from uncertainty and incompleteness due to partial ignorance, which is not considered in available techniques. Furthermore, traditional approaches suffer from unrealistic assumptions regarding the independence of the human factors and associated actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments with evidence theory and to represent the dependency among the human factors and associated actions using a Bayesian Network (BN) approach. The Human Error Probability (HEP) during different phases of an emergency is then assessed using a Bayesian approach integrated with an evidence theory approach. To understand the applicability of the proposed approach, results are compared with an analytical approach: Success Likelihood Index Methodology (SLIM). The comparative study demonstrates that the proposed approach is effective in assessing human error likelihood. In addition to being simple, it possesses additional capability, such as updating as new information becomes available and representing complex interaction. Use of the proposed method would provide an effective mechanism of human reliability assessment in hazardous operations.

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model

This is the second in a series of five papers describing the information, decision, and action in crew context (IDAC) model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, psychological, and physical activities during the course of an accident. This paper identifies the IDAC set of performance influencing factors (PIFs), providing their definitions and causal organization in the form of a modular influence diagram. Fifty PIFs are identified to support the IDAC model to be implemented in a computer simulation environment. They are classified into eleven hierarchically structured groups. The PIFs within each group are independent to each other; however, dependencies may exist between PIFs within different groups. The supporting evidence for the selection and organization of the influence paths based on psychological literature, observations, and various human reliability analysis methodologies is also indicated.

AGAPE‐ET: A Methodology for Human Error Analysis of Emergency Tasks

This article introduces a human error analysis or human reliability analysis methodology, AGAPE-ET (A Guidance And Procedure for Human Error Analysis for Emergency Tasks), for analyzing emergency tasks in nuclear power plants. The AGAPE-ET method is based on a simplified cognitive model and a set of performance-influencing factors (PIFs). At each cognitive function, error-causing factors (ECF) or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and the influencing mechanism of the PIFs on the cognitive function. Then, a human error analysis procedure based on the error analysis factors is organized to cue or guide the analyst in conducting the human error analysis. The method can be characterized by the structured identification of weak points of the task required to be performed and by the efficient analysis process such that the analyst has only to carry out the analysis with the necessary cognitive functions. Through the application, AGAPE-ET showed its usefulness, which effectively identifies the vulnerabilities with respect to cognitive performance as well as task execution, and that helps the analyst directly draw specific error reduction measures through the analysis.