Technique For Human Error Rate Prediction Research Articles

Creating formative HRA dependency models using the HRA dependency idioms and SACADA data, Part II: Model quantification

Human reliability analysis (HRA) identifies the causal factors impacting the occurrence of human failure events and quantifies the human failure event probabilities based on those causal factors, and requires understanding the dependency structures that exist between failure events and causal factors. Many HRA methods incorporate the dependency framework established in the Technique for Human Error Rate Prediction (THERP), which uses simple multipliers on human error probabilities (HEPs) resulting from considering only a few factors. Accordingly, those HRA methods have a limited ability and accuracy when characterizing dependency. This paper presents a methodology for using HRA data to quantify Bayesian networks built from the HRA dependency idioms. The result is an objective and traceable human reliability model that enforces formative, rather than summative, dependency. The results show that baseline HEPs in this model are reasonable, and the data-informed dependency is a significant improvement for the field.

Dependency Idioms for Quantitative Human Reliability Analysis

Human reliability analysis (HRA) is approaching nearly 60 years of reliance on key aspects of the original HRA method Technique for Human Error Rate Prediction (THERP), including its process for analyzing dependency. Despite advances in computational abilities and HRA-relevant techniques, the conceptualization, modeling, and quantification of dependency have remained largely unchanged since the introduction of THERP. As a result, current HRA methods do not consider dependency in a realistic manner, and there remain foundational gaps related to the definition, lack of causality, and quantification for HRA dependency. In this paper, we review the current conceptualization of dependency and demonstrate that current research in dependency is not addressing all of the technical gaps. To address the outstanding technical gaps in HRA dependency, we propose a set of fundamental dependency structures (HRA dependency idioms) that capture the spectrum of relationships possible between HRA variables. The idioms provide a robust logical structure for HRA dependency that emphasizes causality and is based on a causal Bayesian network modeling architecture. The idioms conceptualize and model HRA dependency in an objective, traceable, and causally informed manner that facilitates data-based quantification of HRA dependency.

Quantification of human behavior levels by extending Rasmussen's SRK model and the effects of time pressure and training on the levels switching

Human factor-related accidents account for an increasing portion of the total accidents through the advancing level of system automation. Human reliability becomes the key issue in human-machine systems especially for safety-relevant tasks and operations. Rasmussen's SRK (skill-rule-knowledge) model is well known in the field of human factors. Likewise, it is well known that skill-based behaviors have the highest human reliability, while knowledge-based behaviors are associated with the lowest reliability scores. Although numerous studies exist on human error probability (HEP), correspondingly typically attributed directly or indirectly to these three levels of behavior, a coherent, consistent representation, especially using data sources, has not been available. In this contribution, the quantification of human behavior levels with Rasmussen's SRK model is given based on three databases for the first time. Effects of time pressure and training on human reliability switching are also analyzed based on related publications. To determine the HEP of these three levels, three databases, technique for human error rate prediction (THERP), Savannah river site human reliability analysis (SRS-HRA) and nuclear action reliability assessment (NARA), from human reliability analysis (HRA) methods are used. The procedure contains identifying the tasks including the operator involved and the assumptions the analysts made and classifying the tasks into suitable cognitive behavior mode (CBM). In this case, the relationship between SRK levels and HEP is mapped. The effects of the two in automation context very relevant performance shaping factors (PSFs), time pressure and training/knowledge degradation, on human behavior levels switching are analyzed and the explanations of the SRK switching are presented. In this case, a more general structure is established to illustrate the dynamic behavior of levels switching with six directions under different conditions. From the results we conclude that skill, rule, and knowledge behavior levels are continuous in terms of HEP and therefore allow a new inside into this key aspect of human factor quantification. Based on this analysis the consequences of daily automation in the context of autonomous transport systems in combination with human qualification and reliability degrading are from this specific and in the current automation discussion very intensively discussed. The presented discussion linking SRK levels and HEP gives a new perspective on the foreseeable consequences of further automation in application areas with increasing automation of everyday tasks (like using a highly automated vehicle).

Human Factor Analysis of the Railway Traffic Operators

The human factor is an essential aspect of the operability and safety of many technical systems. This paper focuses on the analysis of human errors in the railway domain. The subject of human reliability analysis is the behavior of operators of station-signaling systems responsible for rail traffic management. We use a technique for human-error rate prediction as the 1st generation human reliability analysis to deal with task analyses, error identification and representation, and the quantification of human error probabilities. The paper contributes to the comparison of three technologically different railway traffic control systems, having different degrees of automation—from the manually operated (electro-mechanical), through semi-automated (relay-based) to almost fully automated (computer-based) station-signaling systems. We observe the frequency of individual operations performed in time intervals and calculate human error probability and human success probability values for each operation. Thus, we can analyze human reliability and compare the workload of operators working with control systems of different degrees of automation.

Dependency definitions for quantitative human reliability analysis

Human reliability analysis (HRA) identifies the causal factors impacting the occurrence of human failure events and quantifies probabilities based on those causal factors. Quantification requires understanding the dependency structures that exist between failure events and causal factors. Most HRA methods incorporate the dependency framework established in the Technique for Human Error Rate Prediction (THERP), which approaches dependency as a simple multiplier on HEPs resulting from considering only a few factors. Accordingly, modern HRA methods have a limited ability and accuracy when characterizing dependency. The fundamental concept of dependency in HRA methods is approaching 60 years without a serious assessment of its veracity or applicability. Misconceptions and confusion surrounding fundamental concepts in HRA are largely responsible for the lack of substantive improvement in the technical basis of dependency. This paper defines dependency for HRA and provides clarification and unification around related concepts. We propose a standardized library of key terms and mathematics to provide a basis for the development of a dependency framework. We clarify the meaning of, and difference between, critical dependency concepts and propose a general lexicography for decomposing scenarios. We present a holistic linguistic and mathematical definition for dependency in HRA.

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.

New method for dependence assessment in human reliability analysis based on linguistic hesitant fuzzy information

Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis.

Belief rule‐based dependence assessment method under interval uncertainty

AbstractHuman reliability analysis (HRA) is of great significance for probabilistic risk assessment, and the technique for human error rate prediction (THERP) has been widely applied to assess the dependence among HRA. However, uncertainties in analyst's judgment and experts' knowledge, especially interval uncertainty in analyst's judgment, have been ignored by existing methods. To this end, the belief rule‐based system is employed to model uncertainties in experts' knowledge in this paper, and the interval belief distribution is used to model interval uncertainty in analyst's judgment. Then, a new belief rule‐based dependence assessment method is proposed, and two case studies are used to illustrate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method could not only model uncertainty using belief rules and interval belief distributions, but also provide a novel and effective way for human reliability analysis.

A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration

Despite the use of automation technology in the maritime industry, human errors are still the typical navigational risk influencing factors in autonomous ships with the third degree of autonomy. However, there is an urgent need for new human error probability assessment focusing on the autonomous cargo ships with human–autonomy collaboration. Hence, to assess these human errors during the emergency response process, a probabilistic model is proposed in this paper. Firstly, the risk factors are identified and classified by analysing the operational process of the Shore Control Centre (SCC). This is followed by the establishment of an event tree model delivered from human errors using Technique for Human Error Rate Prediction (THERP). Furthermore, Bayesian Networks (BNs) model is utilized for the three stages of perception, decision, and execution. Finally, the human errors probabilities are calculated for the mentioned three stages focusing on human–autonomy collaboration. Moreover, the importance of human error factors is quantified with sensitivity analysis, which can provide flexible references for the theoretical construction of the SCC and training of operators. The process was applied to assess the probabilities of human errors focusing on human–autonomy collaboration under the remote navigation mode of an autonomous cargo ship (test ship) in the city of Wuhan, China.

Human error probability quantification for NPP post-accident analysis using Cognitive-Based THERP method

Human Reliability Analysis (HRA) studies human actions from the safety point of view, modeling the most important of them and assessing their probabilities. The Technique for Human Error Rate Prediction (THERP) is a classical HRA approach that models Human Error Probability (HEP) for a specific action regardless of the human cognitive process. Another HRA method is Human Cognitive Reliability (HCR) that expresses the non-response probability as a function of available time regarding the performance type and ignores other affecting factors. In this study, the strengths of the Deterministic Safety Analysis (DSA), THERP and HCR are integrated to form a systematic framework introduced as Cognitive Based THERP (CB-THERP) method to calculate HEP for the Nuclear Power Plant (NPP) post-accident analysis. The calculated values of HEP during the Total Loss of Feedwater (TLFW) accident reveal that a better Human-Machine Interface (HMI) condition reduces HEP. However, operators' higher training level compensates even the worst HMI. In addition, the HMI condition has the greatest impact on an averaged-knowledge Main Control Room (MCR) operator and then an expert and a novice. Dependency almost doesn't influence the worst HMI condition and it much affects highly trained operators when the HMI condition is excellent. Also, the calculated HEP values from the Standardized Plant Analysis Risk-Human Reliability (SPAR-H) and CB-THERP methods for different cases of the scenario have similar trend while THERP calculates a fixed value for them.

Dependence Assessment in Human Reliability Analysis Based on the Interval Evidential Reasoning Algorithm Under Interval Uncertainty

Dependence assessment, which is to assess the influence of the operator’s failure of a task on the failure probability of subsequent tasks, is an important part in Human reliability analysis (HRA). The technique for human error rate prediction (THERP) has been widely applied to assess the dependence in HRA. However, due to the complexity of the real world, various kinds of uncertainty could occur in dependence assessment problem, and how to properly express and deal with uncertainty especially interval uncertainty remains a pressing issue. In this article, a novel method based on the interval evidential reasoning (IER) algorithm is proposed to assess dependence in HRA under interval uncertainty. First, dependence influential factors are identified and their functional relationship is determined. Then, judgments on these factors provided by the analysts are represented using interval belief distributions. Next, the interval evidential reasoning algorithm is employed to aggregate interval belief distributions of different factors according to their functional relationship while considering the credibility of the interval belief distribution. Finally, the conditional human error probability (CHEP) is calculated based on the fused interval belief distribution, where the upper and lower values are determined by assigning belief degree to the highest and lowest grade of the corresponding grade interval, respectively. Two numerical examples demonstrate that the proposed method not only properly deals with interval uncertainty using interval belief distribution and IER algorithm, but also provides a novel and effective way for dependence assessment in HRA.

Treatment of human and organizational factors for multi-unit HRA: Application of SPAR-H method

Human reliability analysis (HRA) refers to a method for evaluating human errors and providing human error probabilities for application in probabilistic safety assessments (PSAs). Since the Fukushima accident, HRA has been considered an important issue in multi-unit PSAs. However, existing HRA methods generally focus on the analysis of human errors in single-unit PSAs, while HRA methods for the multi-unit PSA have not been well established. If an accident occurs that influences more than one unit, the human and organizational factors due to the formation of accident management organizations, the use of shared or mobile equipment, and the influence of a severe accident on another unit may have a crucial impact on the plant safety and the result of the PSA. Therefore, issues have been raised regarding the analysis and evaluation of the human and organizational factors in a multi-unit HRA.This study aims to develop an approach to treat the human and organizational factors for a multi-unit HRA on the basis of Standardized Plant Analysis Risk HRA (SPAR-H) method. This approach is applicable to level 1, 2, and 3 PSA. Firstly, six multi-unit task types were considered based on a previous study that identified and categorized human and organizational factors for multi-unit HRA. Secondly, a task analysis considering HRA event tree analysis, suggested by Technique for Human Error Rate Prediction (THERP), and a timeline analysis were performed for six multi-unit task types. Thirdly, to identify the challenges of the existing SPAR-H method for multi-unit task types, an evaluation of the applicability of the existing SPAR-H method was conducted by qualitative and quantitative analyses. In addition, the dependence assessment approach suggested in the existing SPAR-H was also evaluated in terms of multi-unit situations. Lastly, by treating the multi-unit challenges identified in the previous section, methods to 1) analyze six multi-unit task types and 2) evaluate the dependence between the operator actions were suggested with some examples. The study is expected to contribute to the estimation and calculation of human error probabilities for applications in multi-unit PSAs.

Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. This inspection is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI is reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components, such as FMEA (Failure Modes and Effects Analysis) and THERP (Technique for Human Error Rate Prediction). An example by using qualitative and quantitative assessesments with these two techniques to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues, is presented.

Technique for Early Consideration of Human Reliability: Applying a Generic Model in an Oil Tanker Operation to Study Scenarios of Collision

In the oil sector, analysis, evaluation, and management of risk are vital, considering the accidents potential severity with respect to human life, environment and property. Since most accidents in this sector include human factors, and given the lack of suitable tools for its consideration along the systems life cycle, especially during design phase, the development of models dedicated to human factors in risk analysis is essential. In this context, a technique for early consideration of human reliability (TECHR) was designed for developing a prospective human performance model, which can be exploited in the system design phase and which can be updated along the system life cycle. TECHR is based on the use of expert opinion in relation to systems that operate or have operated in recent years to obtain estimates of the probabilities of the various types of human error which may occur during the performance of a specific action. This paper presents the application of a prospective human performance model—obtained by TECHR—in the study of an oil tanker operation, focusing on human factor quantification in scenarios of collision. In this work, the actions presented in a previous fault tree—for vessel operation—are quantified considering the mentioned model, and the results are discussed in view of the previous results of this fault tree that used the human error probabilities (HEPs) presented in the technique for human error rate prediction (THERP), allowing the comparison of the results obtained by the THERP with the results obtained by the TECHR.

Identification of Operator Reliability on Sewing Department According to Task Analysis

PT. XYZ is one of the textile companies that produces garments exported to some countries . The company has identified Sewing Department has an error rate above the target at around 1.5%. Similar models were worked Line A1 and A7 has a productivity rate is only 77.14% (below the company's target percentage of 80% ) . This study aimed to identify reliability sewing operator so it can be used as a basis for identifying an increase in productivity of the operator at the Department of Sewing at PT. XYZ . This research method using human reliability (Human Reliability Assessment) is based on the task analysis that method Technique for Human Error Rate Prediction (THERP), Method of Human Error Assessment and Reduction Technique (HEART) and the method of Standardized Plant Analysis Risk Human Reliability Assessment (SPAR-H) to measure the reliability of the operator in the task. Fault Tree Analysis (FTA) is used as a technique to analyze the causal relationship of a risk assessment and measurement reliability. Rework type on Line A1 there are 6 tasks and Line A7 there are 5 task . From analysis to Human Error Probability (HEP) on Fault Tree turns HEART method has the highest value compared to SPAR-H and THERP methods. HEP analysis using HEART method s on Line A1 of 0.319 while the Line A7 of 0.399. Potential reworks using SPAR-H Method on Line A1 and A7 is the task 4 with a HEP value on Line A1 of 0.00959 and Line A7 of 0.00209 . HEP analysis using THERP Method for Line A1 of 0.1615 while the Line of 0.2001. Sewing operator reliability in identifying an increase in labor productivity on Line A1 and A7 looks at HEP value for each task performed. On Line A1 and A7 seen that the HEP is below 0.5 means the reliability of operators in doing the task can be improved with the cooperation of all the company components .

Operator response modeling and human error probability in TRIGA Mark II research reactor probabilistic safety assessment

This study presents the quantitative part of the analysis of human actions (HAs) in human reliability analysis (HRA) for a research reactor level 1 probabilistic safety assessment (PSA). In responding to the abnormal conditions, type C HAs which are identified in the previous study are elucidated furthermore for their cognitive response element, prior to the quantification of their human error probabilities (HEPs). The HEPs are afterward quantified by mean of Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method. For the type A and B HAs, their HEPs are quantified by using the Technique for Human Error Rate Prediction (THERP) method. Incorporation of the human failure events (HFEs) and their respective HEPs into the PSA models further allow the evaluation of human contribution to the risk associated with the operation of the research reactor. In this study, it is quantitatively shown that human attribute has major contribution to the risk and with the introduction of nominal level of procedure-performance shaping factor (PSF), risk can be significantly reduced.

Validating THERP: Assessing the scope of a full-scale validation of the Technique for Human Error Rate Prediction

Science-based Human Reliability Analysis (HRA) seeks to experimentally validate HRA methods in simulator studies. Emphasis is on validating the internal components of the HRA method, rather than the validity and consistency of the final results of the method. In this paper, we assess the requirements for a simulator study validation of the Technique for Human Error Rate Prediction (THERP), a foundational HRA method. The aspects requiring validation include the tables of Human Error Probabilities (HEPs), the treatment of stress, and the treatment of dependence between tasks. We estimate the sample size, n, required to obtain statistically significant error rates for validating HEP values, and the number of observations, m, that constitute one observed error rate for each HEP value. We develop two methods for estimating the mean error rate using few observations. The first method uses the median error rate, and the second method is a Bayesian estimator of the error rate based on the observed errors and the number of observations. Both methods are tested using computer-generated data. We also conduct a pilot experiment in The Ohio State University’s Nuclear Power Plant Simulator Facility. Student operators perform a maintenance task in a BWR simulator. Errors are recorded, and error rates are compared to the THERP-predicted error rates. While the observed error rates are generally consistent with the THERP HEPs, further study is needed to provide confidence in these results as the pilot study sample size is small. Sample size calculations indicate that a full-scope THERP validation study would be a substantial but potentially feasible undertaking; 40h of observation would provide sufficient data for a preliminary study, and observing 101 operators for 20h each would provide data for a full validation experiment.

Inclusion of task dependence in human reliability analysis

Dependence assessment among human errors in human reliability analysis (HRA) is an important issue, which includes the evaluation of the dependence among human tasks and the effect of the dependence on the final human error probability (HEP). This paper represents a computational model to handle dependence in human reliability analysis. The aim of the study is to automatically provide conclusions on the overall degree of dependence and calculate the conditional human error probability (CHEP) once the judgments of the input factors are given. The dependence influencing factors are first identified by the experts and the priorities of these factors are also taken into consideration. Anchors and qualitative labels are provided as guidance for the HRA analyst׳s judgment of the input factors. The overall degree of dependence between human failure events is calculated based on the input values and the weights of the input factors. Finally, the CHEP is obtained according to a computing formula derived from the technique for human error rate prediction (THERP) method. The proposed method is able to quantify the subjective judgment from the experts and improve the transparency in the HEP evaluation process. Two examples are illustrated to show the effectiveness and the flexibility of the proposed method.

Integration of HOFs into ATEX Risk Assessment Methodology

The current ATEX (Explosive Atmosphere) risk assessment methodology relies on a semi-quantitative approach based on the following indexes: probability of an explosive atmosphere formation, probability of the presence of an effective ignition source and consequences. The whole risk assessment procedure can be separated into four steps ---- area classification, ignition sources identification, consequence analysis, and risk evaluation. However, when reviewing the whole ATEX risk assessment procedures and their results, despite many operations on plant and equipment containing dangerous substances are performed by operators, the human and organizational influences are neglected. The study proposed in this paper is to develop an ATEX risk assessment methodology with the integration of human and organizational factors (HOFs), in order to provide an advanced methodology able to analyse the HOFs influences on ATEX hazards. The developed ATEX risk assessment methodology introduces in the procedure the Technique for Human Error Rate Prediction (THERP) to identify human and organizational weaknesses, and also provides a tool for calculating the human error probability (HEP) in the first two risk steps ---- area classification and ignition sources identification. During the risk evaluation procedure, human risk level, based on the results of HEP, has been introduced into the original ATEX risk assessment methodology.

Assessment of Flight Crew Errors Based on THERP

The purpose of this study is to reduce the aviation accidents and incidents induced by flight crew errors. A flight crew errors analysis and classification system is developed preliminary. In combination with aviation flight accidents exploration, and interviews with pilots, the human error modes which will occur in the flight task are identified. A quantitative method to assess flight crew errors is established on Technique For Human Error Rate Prediction (THERP). In a case study, the takeoff task is analyzed, the human error modes and consequences are identified, and the failure probability of takeoff task is calculated. Those results are useful for SSA (System Safety Assessment) to improve the aircraft safety design.