Human Error Probability Quantification Research Articles

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

Human reliability analysis studies from simulator experiments using Bayesian inference

Probabilistic Safety Assessment (PSA) of complex facilities is performed to arrive at the risk posed by them. PSA also accounts for the contribution of the human errors towards the overall risk through Human Reliability Analysis (HRA) in terms of Human Error Probability (HEP). Human operators are part of the system and do not work in isolation. Their performance is influenced by the context in which the actions are performed. As a result, quantification of HEP requires operator performance data under the given context. Some good sources of operator performance data are plant's operation data, simulator data and expert judgement. The plant operation data pertaining to HRA is generally sparse. In this situation, a full scope plant simulator provides a good alternative for operator performance data generation. Many of the currently practised HRA methods have been developed by combining the empirical evidence with expert judgement and contain a lot of uncertainty in their estimates. Bayesian inference is suitable for updating the prior HRA estimates with the simulator evidence to obtain the posterior HEP. In this study, posterior HEP has been calculated for postulated accident scenarios in advanced reactor (first of its kind) at design stage, using plant simulator.

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

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.

HEP quantification strategy based on modified CREAM

The accurate quantification of human error probability (HEP) has long been a main pursuit for most human reliability analysis (HRA) methods. This paper proposes a strategy to analyze and predict human error on the basis of a further modification of the existing cognitive reliability and error analysis method (CREAM) in HRA. Through providing a broader definition for the key parameters used in the quantification method, this paper produces a relatively flexible strategy to determine the nominal HEP (HEP 0 ). Basing on this strategy, the actual quantification of HEP in CREAM is able to be applied to more verified conditions. To prove the validity of the method proposed, a case study of spacecraft launch is also introduced, in which the prediction derived from the method is proved consistent with real field data.

Performance shaping factors in the human error probability modification of human reliability analysis

Human-induced accidents indicate the importance of human reliability analysis (HRA) in reducing and eliminating human errors, thus improving the reliability of human–machine systems. HRA takes both qualitative and quantitative approaches to determine the error of the operators and the contexts in which tasks are performed. To ensure that HRA results can objectively evaluate human error behaviors, the quantification of human error probability (HEP) is typically based on the qualitative analysis of human factors and task contexts and is further refined by performance shaping factors (PSFs). A good HEP process development includes the selection of PSFs, the evaluation of PSFs and the quantification strategy of HEP. A variety of HEP quantification analyses based on PSFs has been widely adopted in contemporary HRA studies. This work reviews three major quantification strategies used in HRA methods. Additionally, we generalize the modification of HEP with PSFs into a paradigm.

Human Error Probability Quantification using SPAR-H Method: Total Loss of Feedwater case study for VVER-1000

The primary side Feed & Bleed (F&B) recovery strategy is the last device to avoid core damage in the event of loss of Ultimate Heat Sink (UHS) accident. In the context of accident management, besides multiple safety systems’ well-functioning, human factor has a significant role in avoiding the extreme scenarios. In this research, it is tried to quantify Human Error Probability (HEP) of operators’ interference to initiate F&B recovery procedure when the Total Loss of Feedwater (TLFW) accident occurs in a typical VVER-1000 Nuclear Power Plant (NPP) via Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) Method. Among those factors that affect human performance is the available time for the operators’ late interference in order to reestablish natural circulation to remove the residual heat after the reactor scrammed which is computed using the best estimate code RELAP5 Mod 3.2. It is revealed that the diagnosis and the action HEPs of the operators are highly dependent and the combined HEP value is too risky with an unacceptable amount of uncertainty. It is concluded that the accident management procedure must be redesigned so as to eliminate diagnosis and action dependency.

A new hybrid approach to human error probability quantification–applications in maritime operations

Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors.

A classification scheme of erroneous behaviors for human error probability estimations based on simulator data

Because it has been indicated that empirical data supporting the estimates used in human reliability analysis (HRA) is insufficient, several databases have been constructed recently. To generate quantitative estimates from human reliability data, it is important to appropriately sort the erroneous behaviors found in the reliability data. Therefore, this paper proposes a scheme to classify the erroneous behaviors identified by the HuREX (Human Reliability data Extraction) framework through a review of the relevant literature. A case study of the human error probability (HEP) calculations is conducted to verify that the proposed scheme can be successfully implemented for the categorization of the erroneous behaviors and to assess whether the scheme is useful for the HEP quantification purposes. Although continuously accumulating and analyzing simulator data is desirable to secure more reliable HEPs, the resulting HEPs were insightful in several important ways with regard to human reliability in off-normal conditions. From the findings of the literature review and the case study, the potential and limitations of the proposed method are discussed.

Quantification of human error probability towards the gas inerting process on-board crude oil tankers

This paper presents an approach to quantify human error probability in the gas inerting operation of crude oil tankers since it is one of the most critical processes on-board ships. The maritime regulatory bodies require the application of the use of inert gas on newly built tankers, and most existing tankers over 20,000dwt, since the gas inerting operation prevents fire and chemical reaction inside the cargo tanks. Despite its operational benefits, it may cause oxygen deficiency inside the tanks and damage human health as well as the marine environment. Therefore, it is quite significant for marine safety practitioners to predict human error probability (HEP) during the gas inerting operation. Unlike conventional HEP assessment methods, this paper contains a quantitative approach to systematically predict human error for designated tasks and ascertain the desired safety control level in crude oil tanker ships. To achieve this goal, a quantification of human error probability approach is proposed. In view of the findings, human error reduction measures are recommended. As a result, the paper is expected to contribute to the improvement of maritime safety, protection of the environment, and the reduction of loss of life on-board crude oil tankers.

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 Soft Control Model for Human Reliability Analysis in APR-1400 Advanced Control Rooms (ACRs)

Human reliability analyses play very important roles in safety analyses for NPP operations. However, scarcity of the basic human error probability data of soft control human actions has been considered as a main bottleneck for HRAs of the soft control tasks in NPPs. In this study, the soft control tasks are analyzed and modelled to be used for development of the basic human reliability data and quantification of human error probability. Sub-tasks comprising a soft control are modelled to be observable and distinguishable as unit tasks so that the basic human error probabilities for the sub-tasks could be observed and calculated in simulator-based experimental or real field operational studies. Safety-grade and non-safety grade soft controls are modelled for controls of safety and non-safety components, respectively. Possible error modes and propagation to another unintended control action, impact on soft control operation, and final error type are analyzed to provide helpful information for applications to the final human error quantification and reduction. Considerations are provided for the soft control models developed to be used in HRAs. The results of this study would be used for development of a human reliability model and basic human reliability data and quantification and reduction of human error probability during the soft control in various large-scale process control systems such as nuclear power plant (NPPs), chemical processing plants, oil processing plant, and large military systems.

The effect of job rotation during assembly on the quality of final product

In this study, the effect of job rotation techniques on the final product's quality is investigated in the case of human based assembly environments. High product diversification challenges the limited human capabilities by exposing them to an environment of high fatigue accumulation and high task repetitiveness. The result is a reduction in the final product's quality due to human errors during the assembly. This paper investigates the effect of applying job rotation techniques for the derivation of the operators’ schedules. In this case, the fatigue distribution and the enrichment of the working environment can lead to the reduction of assembly errors. Human error probability quantification techniques have been applied to predict the performance of the assembly line, under a given workload in both cases – with and without job rotation. The approach takes into consideration the characteristics of the operator, the product as well as the assembly environment in order for the probability of error occurrence to be calculated for each assembly task. The method is applied on a case study, involving the assembly of heavy vehicles. The findings indicate that the adoption of job rotation techniques can significantly enhance product quality by drastically reducing the total number of assembly errors.

Validation of Human Error Probabilities with Statistical Analysis of Misbehaviours

There is a lot of methods, how to determine human error probability. The methods have been used to predict human error of high risk operation. The quantification of human error probability is based on having statistically relevant data of human tasks and the associated error rates, using of databases and call off the required values by expert estimation. The issue of assessing the reliability of operators includes, except of operator skills, many other impacts. They can be characterized by factors that are most commonly known as Performance Influencing Factors (PSF). PSF are the factors of working system that affect or could affect the performance of operators, their reliability, and their recovery from encountered error. For a very simple task, there are many possible results from each method, and one of the reasons is, that it is difficult to validate value of human error probability. But this problem can be solved with statistical analysis of misbehaviours of specific employees. Statistical approach allows validating the likelihood of human error (for specific employee) both in qualitative tasks (e.g. opening of valve) as well as quantitative tasks (e.g. error of reading the value from the measuring instrument in the process unit operation). This article aims to propose simple approaches for validation of human errors.