Human Error Probability Estimation Research Articles

Human Reliability Analysis Based on Human Abilities Theory Model

In this paper, we introduce a new Human Reliability Analysis method based on a Human Abilities Theory Model (HRA-HAT). This approach simultaneously addresses cross-cutting aspects of the main issues and criticisms of human reliability models: 1) model's theoretical bases (including taxonomy and concept specificity); 2) definition and use of performance shaping factors; and 3) HRA quantification. HAT is used to support method parameters selection and the consequent data collection procedure. The resulting methodology works on the basis of three main stages: 1) Fuzzy Cognitive Task Analysis; 2) Fuzzy Operator Analysis; and 3) Fuzzy Human Error Probability estimation based on Human Abilities Theory model. “Operator Analysis” is a new stage of HRA that reduces uncertainty about individuals’ actual behavior in specific situations by focusing on this essential component of sociotechnical systems. To exemplify method implementation procedures, an illustrative example is analyzed. Finally, methodology sensibility and uncertainty are explored in order to prompt HRA-HAT validation. Since this methodology was conceived as an approach applicable to several fields, it is expected to be revised and adapted for further practical applications.

A method of human reliability analysis and quantification for space missions based on a Bayesian network and the cognitive reliability and error analysis method

AbstractAnalyses of human reliability during manned spaceflight are crucial because human error can easily arise in the extreme environment of space and may pose a great potential risk to the mission. Although various approaches exist for human reliability analysis (HRA), all these approaches are based on human behavior on the ground. Thus, to appropriately analyze human reliability during spaceflight, this paper proposes a space‐based HRA method of quantifying the human error probability (HEP) for space missions. Instead of ground‐based performance shaping factors (PSFs), this study addresses PSFs specific to the space environment, and a corresponding evaluation system is integrated into the proposed approach to fully consider space mission characteristics. A Bayesian network is constructed based on the cognitive reliability and error analysis method (CREAM) to model these space‐based PSFs and their dependencies. By incorporating the Bayesian network, the proposed approach transforms the HEP estimation procedure into a probabilistic calculation, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex space environment. More importantly, by acquiring more information, the HEP estimates can be dynamically updated by means of this probabilistic calculation. By studying 2 examples and evaluating the HEPs for an International Space Station ingress procedure, the feasibility and superiority of the developed approach are validated both mathematically and in a practical scenario.

BAYES-HEP: Bayesian belief networks for estimation of human error probability

Human errors contribute a significant portion of risk in safety critical applications and methods for estimation of human error probability have been a topic of research for over a decade. The scarce data available on human errors and large uncertainty involved in the prediction of human error probabilities make the task difficult. This paper presents a Bayesian belief network (BBN) model for human error probability estimation in safety critical functions of a nuclear power plant. BBN is a powerful tool and has been widely used for risk and reliability analysis framework under the conditions of uncertainty. BBNs are joint probability distribution between multiple variables (i.e., performance shaping factors, error modes, error mechanisms, etc.) expressed as directed acyclic graphs consisting of nodes and arcs. Nodes represent system components, and arc represents relationship among them with conditional probability tables. This network functions as an engine for calculation of probability of events given the observation of other events in the same network. BBN can be used to model the uncertainty parameters in a system. Sensitivity analysis can also be performed to study how uncertainty in the model output can be attributed to different sources of uncertainty in the model input. It is frequently applied in real-world situations such as diagnosis, forecasting, and environment, but received less attention in the area of human reliability. In view of its natural architecture, BBN is found to be more appropriate when there is scarce, multi-source data available as in the case of human error data. Further, the probabilistic approach of BBN is best suited for safety assessment to predict system reliability and estimate the probability of consequence of an event. BBN has been adapted to model human factors by taking into account the different parameters and their mutual influences. BBN can also be used to identify the potential human factors leading to significant reduction of accident probability during the operation of NPP. Several researchers have developed advanced HRA models to estimate the error probability but most of the HRA model requires expert judgment at several stages and hence it is subjective. Other models such as HCR and CREAM are not applicable for all scenarios. Limited studies are carried out to eliminate the need of human expert in the human reliability methods. This paper introduces a method that uses the available historical data and provides efficient technique for automated evaluation of human error probability using BBN. In this respect, human cognitive reliability (HCR) has been identified as a suitable model to estimate the mean time required for human intervention and in the selection of human action to estimate HEP in the model. The developed model using BBN would help to estimate HEP with limited human intervention. A step-by-step illustration of the application of the method and subsequent evaluation is provided with a relevant case study and the model is expected to provide useful insights into risk assessment studies.

A hybrid human error probability determination approach: The case of cargo loading operation in oil/chemical tanker ship

HEART (human error assessment and reduction technique) is widely recognized a robust empirical tool in determination of HEP (human error probability) since prediction of human error is of paramount importance in marine industry (Williams, 1988). The method is successfully applied to a variety of domains such as aviation, railway, petrochemical, nuclear power plants, etc. The method has two fundamental parameters; generic error probability (GEP) and error-producing conditions (EPCs). The EPCs are the factors that affect the human performance negatively. The paper deals in principle with one of the methods used for human error probabilities estimation and it suggests its enhancement using analytic hierarchy process (AHP) (Saaty, 1980). Thus, quantification of the subjective judgements of experts during assessed proportion of affect (APOA) calculation is achieved in the course of HEP. To demonstrate the proposed approach, cargo loading operation in oil/chemical tanker ship is selected since the process is very critical to prevent loss of life and marine environment pollution. Consequently, besides its theoretical insight, the paper has practical outcomes to safety practitioners and maritime professionals for estimation of HEP in a specific task.

A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy‐OR Gates

Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach. Copyright © 2016 John Wiley & Sons, Ltd.

Human Reliability Analysis of Roof Bolting Operation in Underground Coal Mines

Investigations have shown that human error is the most common cause of roof bolting injuries. Human error probability estimation has become a critical issue for human reliability analysis (HRA) of roof bolting operation. Specialist judgment plays a crucial role in quantifying human error probability in the field because of limited availability of empirical data. However, the aggregation of specialist judgment is typically not carried out in a formal way in HRA. In this paper, an approach to combine Bayesian methodology and the success likelihood index method was to build a computable model using information from specialists for HRA of roof bolting operation. A numerical example was used to illustrate the application of the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.

Human Error Probability Estimation in Atex-hmi Area Classification: from Therp to Fuzzy Cream

ATEX (explosive atmosphere) risk assessment is required when any equipment or system can potentially cause explosive atmospheres. Although many operations are performed by operators, influences from human and organizational factors (HOFs) are mostly neglected. In order to address HOFs influence, the ATEX-HMI (Human-Machine Interaction) methodology is proposed: it aims at integrating HOFs into each step of the standard ATEX risk assessment. The first step faced is Area classification: the ATEX-HMI methodology introduces the Human Error Probability (HEP) into the zone calculation procedure. Human Reliability Analysis (HRA) is the main method for the HEP assessment, thus two HRA methods were applied for the ATEX-HMI methodology. The first one, THERP, demonstrated an efficient quantitative precision, but it was not easily applicable to real industries. Then, the Cognitive Reliability and Error Analysis Method (CREAM) was adopted, together with a complementary FUZZY application for quantitative analysis. The present paper introduces the FUZZY CREAM application, also showing a comparative analysis of THERP and FUZZY CREAM, on the basis of a food plant case study.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

Human error probability estimation by coupling simulator data and deterministic analysis

Operator error in diagnosis and execution of task have significant impact on Nuclear Power Plant (NPP) safety. These human errors are classified as mistakes (rule base and knowledge based errors), slip (skill based) and lapses (skill based). Depending on the time of occurrence, human errors have been categorized as i) Category ‘A’ (Pre-Initiators): actions during routine maintenance and testing wherein errors can cause equipment malfunction ii) Category ‘B’ (Initiators): actions contributing to initiating events or plant transients iii) Category ‘C’ (Post-Initiators): actions involved in operator response to an accident. There have been accidents in NPPs because of human error in an operator's diagnosis and execution of an event. These underline the need to appropriately estimate HEP in risk analysis. There are several methods that are being practiced in Probabilistic Safety Assessment (PSA) studies for quantification of human error probability. However, there is no consensus on a single method that should be used. In this paper a method for estimating HEP is proposed which is based on simulator data for a particular accident scenario. For accident scenarios, the data from real NPP control room is very sparsely available. In the absence of real data, simulator based data can be used. Simulator data is expected to provide a glimpse of probable human behavior in real accident situation even though simulator data is not a substitute for real data. The proposed methodology considers the variation in crew performance time in simulator exercise and in available time from deterministic analysis, and couples them through their respective probability distributions to obtain HEP. The emphasis is on suitability of the methodology rather than particulars of the cited example.

A Bayes-SLIM based methodology for human reliability analysis of lifting operations

Investigations have shown that human error (HE) is the most common cause of accidents involving lifting operations. So it is essential to conduct human reliability analysis (HRA) in lifting operations. The estimation of human error probabilities (HEPs) is a key to HRA. In this paper, five useful performance shaping factors (PSFs) of lifting operations were introduced and an approach to combine Bayesian methodology and the success likelihood index method (SLIM) was applied to determine the HEPs of various lifting operation errors. A numerical example was used to illustrate the application of the proposed methodology. Relevance to industryReliability analysis of crane lifting operation has traditionally been difficult without considering human error. The present study suggests that managers could use the Bayesian-SLIM methodology to conduct human reliability analysis and minimize human error in crane lifting operation.

The SACADA database for human reliability and human performance

Lack of appropriate and sufficient human performance data has been identified as a key factor affecting human reliability analysis (HRA) quality especially in the estimation of human error probability (HEP). The Scenario Authoring, Characterization, and Debriefing Application (SACADA) database was developed by the U.S. Nuclear Regulatory Commission (NRC) to address this data need. An agreement between NRC and the South Texas Project Nuclear Operating Company (STPNOC) was established to support the SACADA development with aims to make the SACADA tool suitable for implementation in the nuclear power plants' operator training program to collect operator performance information. The collected data would support the STPNOC's operator training program and be shared with the NRC for improving HRA quality. This paper discusses the SACADA data taxonomy, the theoretical foundation, the prospective data to be generated from the SACADA raw data to inform human reliability and human performance, and the considerations on the use of simulator data for HRA. Each SACADA data point consists of two information segments: context and performance results. Context is a characterization of the performance challenges to task success. The performance results are the results of performing the task. The data taxonomy uses a macrocognitive functions model for the framework. At a high level, information is classified according to the macrocognitive functions of detecting the plant abnormality, understanding the abnormality, deciding the response plan, executing the response plan, and team related aspects (i.e., communication, teamwork, and supervision). The data are expected to be useful for analyzing the relations between context, error modes and error causes in human performance.

Bayesian estimator of human error probability based on human performance data

A Bayesian method for estimating human error probability (HEP) is presented. The main idea of the method is incorporating human performance data into the HEP estimation process. By integrating human performance data and prior information about human performance together, a more accurate and specific HEP estimation can be achieved. For the time-unrelated task without rigorous time restriction, the HEP estimated by the common-used human reliability analysis (HRA) methods or expert judgments is collected as the source of prior information. And for the time-related task with rigorous time restriction, the human error is expressed as non-response making. Therefore, HEP is the time curve of non-response probability (NRP). The prior information is collected from system safety and reliability specifications or by expert judgments. The (joint) posterior distribution of HEP or NRP-related parameter(s) is constructed after prior information has been collected. Based on the posterior distribution, the point or interval estimation of HEP/NRP is obtained. Two illustrative examples are introduced to demonstrate the practicality of the aforementioned approach.

Human error probabilities from operational experience of German nuclear power plants

We report about a project aimed to collect human reliability data from the operational experience of German nuclear power plants. Its objective is the validation and extension of existing human reliability databases (in particular, the THERP database). A method utilizing the German licensee event report system to gather the data is described. For certain tasks with specific attributes this method allows to determine the number of times the task was performed in the past, as well as the number of errors that occurred. A statistical method to estimate the corresponding human error probability (HEP) based on these numbers is provided. We have applied this method to the reportable events stored in the database collecting the reportable events in German nuclear installations. In this way up to now 37 HEPs for a wide variety of tasks were obtained, together with information about relevant performance shaping factors. We discuss these HEP estimates and compare them to the THERP database if it provides a HEP for the task in question. In all except three cases we find an agreement within the uncertainty bounds. Moreover, we contribute 21 HEP estimates for which the THERP handbook provides no data, so they serve to extend the THERP database, among them a number of memory related errors. Therefore, this data may serve as an input for the discussion of second generation HRA methods.

Human reliability analysis data obtainment through fuzzy logic in nuclear plants

Human error has been recognized as an important factor for many industrial and nuclear accidents occurrence. Human error data is scarcely available for different reasons among which, lapses in historical database registry methodology is an important one. Human Reliability Analysis (HRA) is an usual tool employed to estimate the probability that an operator will reasonably perform a system required task in required time without degrading the system. This meta-analysis requires specific Human Error Probability estimates for most of its procedure. This work obtains Human Error Probability (HEP) estimates from operator's actions in response to emergency situations hypothesis on Research Reactor IEA-R1 from IPEN, Brazil. Through this proposed methodology HRA should be able to be performed even with shortage of related human error statistical data. A Performance Shaping Factors (PSF's) evaluation in order to classify and estimate their influence level onto the operator's actions and to determine their actual state over the plant was also done. Both HEP estimation and PSF evaluation were done based on expert judgment using interviews and questionnaires. Expert group was established based on selected IEA-R1 operators, and their evaluation were put into a knowledge representation system which used linguistic variables and group evaluation values that were obtained through Fuzzy Logic and Fuzzy Set theory. HEP obtained values show good agreement with literature published data corroborating the proposed methodology as a good alternative to be used on HRA.

Estimating Human Error Probability using a modified CREAM

Human Error Probability (HEP) point estimation is important for Probabilistic Safety Assessment (PSA) of socio-technical systems. We present a modified basic method of CREAM to provide the point estimation of HEP for PSA. Five acknowledged assumptions are introduced firstly and the HEP point estimation formula is elicited based on them. Furthermore, the reasonability of the method is discussed and the consistency with other two benchmarking HRA methods, THERP and HEART is validated. Finally, a simple example about starting up the submarine's engine is introduced and the probability of the error forgetting the warm operation is calculated using the modified method. The result of the method is consistent with the recorded human performance data and THERP.

Method of estimating human error probabilities in construction for structural reliability analysis based on analytic hierarchy process and failure likelihood index method

Human error (HE) is the most important factor influencing on structural safety because its effect often exceeds the random deviation. Large numbers of facts have shown that structural failures may be caused by the gross error due to HE. So it is essential to analyze HE in construction. The crucial work of human error analysis (HEA) is the estimation of human error probability (HEP) in construction. The method for estimating HEP, analytic hierarchy process and failure likelihood index method (AHP-FLIM), is introduced in this paper. The method also uses the process of expert judgment within the failure likelihood index method (FLIM). A numerical example shows the effectiveness of the methods proposed.

Human Error Probability Estimation for Process Risk Assessment with Emphasis on Control Room Operations

Human Error Probability Estimation for Process Risk Assessment with Emphasis on Control Room Operations

Inclusion of human errors assessment in failure frequency analysis—A case study for the transportation of ammonia by rail in Malaysia

AbstractRisk is defined as the probability of a specific adverse event occurring within a specific period, while Quantitative risk assessment (QRA) is the development of a quantitative estimate of risk based on engineering evaluation and mathematical techniques by combining estimate of incident consequences and frequencies. In view of the increase in the use of railways as the mode of transportation for hazardous materials throughout the world, the associated risk analysis should be taken into concern. In this study, the failure frequency of the transportation of ammonia from the Petronas fertilizers Kedah (PFK) plant in Gurun (Northern part of Peninsular Malaysia), to the Chemical Company Malaysia (CCM) fertilizer's facilities in Port Klang (South‐western part of Peninsular Malaysia) was evaluated by incorporating the human error assessment. The study highlighted the importance of human error contributions in the failure frequency analysis and its impact on the selected failure scenarios. Besides, it also shows that the application of the human error assessment and reduction technique (HEART), which is a useful human reliability analysis tool, should be used in parallel with the fuzzy arithmetic approach to reduce the uncertainties involved in the estimation of human error probabilities, and hence, to reduce the likelihood of incorrect risks estimates being assessed. The results suggested that the commonly applied approach in quantitative risk assessments, which only consider equipment failures in the failure frequency estimations, are clearly an underestimate of the potential causes of failures leading to hazardous material releases, and hence, the calculated risks based do not reflect the actual risks. © 2008 American Institute of Chemical Engineers Process Saf Prog, 2009

Associations between Errors and Contributing Factors in Aircraft Maintenance

In recent years cognitive error models have provided insights into the unsafe acts that lead to many accidents in safety-critical environments. Most models of accident causation are based on the notion that human errors occur in the context of contributing factors. However, there is a lack of published information on possible links between specific errors and contributing factors. A total of 619 safety occurrences involving aircraft maintenance were reported using a self-completed questionnaire. Of these occurrences, 96% were related to the actions of maintenance personnel. The types of errors that were involved, and the contributing factors associated with those actions, were determined. Each type of error was associated with a particular set of contributing factors and with specific occurrence outcomes. Among the associations were links between memory lapses and fatigue and between rule violations and time pressure. Potential applications of this research include assisting with the design of accident prevention strategies, the estimation of human error probabilities, and the monitoring of organizational safety performance.

Application of the Saint Code in Modelling and Analyzing Some Human Tasks in Nuclear Power Plants

Two techniques useful for the simulation and analysis of human performance in tasks involving nuclear power plant operation, maintenance and testing are evaluated. The SAINT and THERP techniques are compared with respect to their relevance to conducting task analysis, estimation of human error probabilities and accounting for performance shaping factors in nuclear power plants. The results show that the SAINT is more flexible and has promising features for human engineering studies of complex systems when compared to the static THERP technique currently used in nuclear safety analysis.