Standardized Plant Analysis Risk-Human Reliability Research Articles

Analysing human error contribution to ship collision risk in congested waters under the evidential reasoning SPAR-H extended fault tree analysis

Human error plays a crucial role in maritime transportation risk analysis, as a significant percentage of accidents, including collisions, groundings, capsizing, fires, and explosions, can be attributed to human errors. However, obtaining a dataset that quantifies human error probabilities for maritime risk analysis is challenging due to commercial constraints. To address this issue, this paper proposes a conceptual framework that integrates evidential reasoning (ER) and the standardized plant analysis risk-human reliability analysis (SPAR-H) method to quantify human errors, while employing fault tree analysis (FTA) to predict risk. The specific focus of this study is ship collision risk in congested waters, which serves as a demonstration case to showcase the proposed method and illustrate a detailed analysis of collision risk. The findings reveal that “inadequate watchkeeping due to sole lookout”, “improper RADAR monitoring”, and “ineffective execution of COLREG-related actions” are the most significant human errors contributing to collision risk in congested waters. The outcomes of this research provide valuable insights for ship owners, safety professionals, ship masters, inspectors, and researchers in the maritime industry. The findings can assist in minimizing collision risk and improving navigational safety in congested waters.

Handling dependencies among performance shaping factors in SPAR-H through DEMATEL method

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

Dynamic risk assessment of plugging and abandonment operation process of offshore wells based on Dynamic Bayesian Network

The plugging and abandonment (P&A) operation is required when an offshore well is abandoned. This study proposes an analysis method based on dynamic Bayesian network (DBN) to assess the dynamic risk of riserless well intervention systems performing the P&A operation. First, each phase of P&A operation is depicted by a GO model. The fault tree (FT) model of each mission execution system is constructed to analyze the equipment factors leading to the failure of the mission. The mirror DBN model is established from the GO and FT models according to the mapping algorithm and the parameters of the DBN model are obtained by mapping according to the GO and FT model parameters. A complete risk assessment model is established. Next, the standardized plant analysis risk-human reliability analysis method is used to quantify the probabilities of human factors in the DBN model. Finally, the dynamic risk of the operation process is assessed. The developed DBN is verified using a method based on three axioms. Results show that the reservoir abandonment phase has the greatest effect on P&A operation process risk. Sensitivity analysis is performed to identify critical equipment of the system. The proposed method can be a useful tool to assess the dynamic risk of P&A operation process.

Dynamic risk assessment of deepwater drilling using data-based and probabilistic approach

Risks associated with deepwater drilling are dynamic because of various accident-causing factors such as equipment failures, abnormal processes, and operator errors. All these factors should be considered during the implementation of dynamic and quantitative risk assessment (DQRA) for drilling operations. Dynamic Bayesian network (DBN) can graphically present the cause-effect relationships among different types of risk influencing factors (RIFs). Hence, this study developed a four-step DBN model for the DQRA of deepwater drilling. Firstly, multi-type contributing RIFs were identified according to the process flow. Subsequently, a network structure was developed to present the potential accident scenarios and capture the interdependencies among the RIFs. Thereafter, the probabilities of equipment failures, abnormal processes, and operator errors were determined using the probabilistic, data-based, and Standardized Plant Analysis Risk—Human Reliability Analysis (SPAR-H) approach, respectively. Finally, DBN inference was performed to evaluate the probabilistic risk of drilling operations. The model was applied to a case study of DQRA for managed pressure drilling (MPD), where the calculated initial blowout probability was 9.30 × 10−5, whereafter it was updated dynamically. This case study demonstrates the practicability of the proposed approach. This study contributes to a systematic investigation of the role of multisource data in DQRA using a full DBN approach. The assessment results can provide early warnings for practitioners to implement risk elimination or mitigation measures in real time.

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H.

HighlightsA BN version of the SPAR-H model is used to predict and warn of human errors to avoid maritime accidents and ensure the safety of seafarers.Performance-shaping factors (PSFs) are used as factors contributing to unsafe crew acts (UCAs).The conditional probabilities quantitatively describe the relationships among PSFs, UCAs, and human errors.The method offers a point for translating the research model into practical application.Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk–Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers’ unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers’ unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

Develop a framework of human reliability analysis to evaluate the human error probability in DMCR of NPPs

ABSTRACT Human reliability analysis (HRA) has an important effect on the safety of nuclear power plants (NPPs). However, the operating environment in the digital main control room of NPPs has changed with the application of digital interface. Therefore, this paper developed a framework of HRA to evaluate human error probability (HEP) under the condition of digital interface control. Firstly, the hierarchical task analysis method was used to decompose the task to determine a series of sub-tasks. Secondly, the HEPs of diagnosis and execution were calculated based on the standardized plant analysis risk-human reliability analysis method. Finally, the final HEP of task was calculated by considering the recovery failure probability of human error. The experimental results showed that the HEP evaluation result of the developed method was consistent with the statistical method result. It indicated that the developed HRA method can be used to evaluate the HEP of task.

Application of a Bayesian network to quantify human reliability in nuclear power plants based on the SPAR-H method

Human error is an important factor leading to nuclear power plant (NPP) accidents. Human reliability analysis (HRA) is considered an effective method to reduce human error. Therefore, this article proposes a method to quantify human reliability based on the standardized plant analysis risk–human reliability analysis (SPAR-H) method. Firstly, the method used the performance shaping factors of SPAR-H to build a human reliability model. Secondly, the triangular fuzzy number was used to quantify the qualitative information of root nodes, and the fuzzy IF–THEN rule was used to determine the prior probability distribution of intermediate nodes. Finally, Bayesian reasoning was used to quantify human reliability based on the human reliability model. The result of the developed method is consistent with the result of cognitive reliability and error analysis methods (CREAM). The developed method can be used as a tool to quantify human reliability in the NPP system.

Adaptation of the standardized plant analysis–risk human reliability analysis technique for the surgical setting: expert judgment approach

Objectives. Application of human reliability analysis (HRA) techniques originally developed for industrial settings to the healthcare sector may be controversial in terms of reliability and methodological level. The aim of the present study was to adapt a standardized plant analysis risk–human reliability analysis (SPAR-H) technique for application in surgical settings through suggesting more context-specific definitions for performance shaping factors (PSFs), designing precise levels and elicitation of multipliers through a domain expert judgment approach. Methods. A ratio magnitude estimation approach was used for carrying out domain expert judgment for multiplier elicitation. Experts from four teaching hospitals participated in the present study. Intra-class correlation was used in order to examine the inter-rater reliability of the estimated multipliers for each level of diagnosis and action task type. Results. Available time, threat stress, task complexity, experience/training, procedures, working conditions, human–machine interface, fatigue and teamwork were the nine suggested PSFs for the adapted SPAR-H technique. Conclusion. Context-specific definitions of the PSFs can enhance the reliability of human error probability assessments. Eventually, it could be concluded that multiplier elicitation through domain expert judgment is an efficient approach for adaptation of the HRA techniques for application in specific contexts.

Investigation of the Occupational Hygiene Errors in the Tasks of Overhead Crane Operator Using Standardized Plant Analysis Riskhuman Reliability Analysis Technique in One Steel Industry

Investigation of the Occupational Hygiene Errors in the Tasks of Overhead Crane Operator Using Standardized Plant Analysis Riskhuman Reliability Analysis Technique in One Steel Industry

A proposed improvement for the design of safety injection system in VVER-1000/V446 reactor

If the Total Loss of Feedwater (TLFW) accident occurs in a VVER reactor, the primary side Feed & Bleed (F&B) recovery strategy prevents core damage by reducing the reactor's pressure by opening the pressurizer's safety valves and establishing core cooling by the Emergency Core Cooling System (ECCS). Although the strategy saves the core, the leaked coolant together with the containment spray system pollutes the containment environment, deactivates electrical devices, and jeopardizes the containment integrity by the release of the generated hydrogen during the core re-flooding. Further, Steam Generators (SGs) would be exposed to excessive thermal stress because of drying out, making use of recovered feedwater too risky because of the primary to secondary side leakage possibility. ECCS in VVER-1000/V446 has low-pressure Hydro accumulators (HAs) that could carry out secondary side F&B through SGs. This feasibility study uses Deterministic Safety Analysis (DSA) to show that the secondary side F&B recovery strategy can provide 6 h after losing Emergency Feedwater (EFW) which is a significant time to recover the failed safety systems. This strategy is more significant, especially if the TLFW accident is followed by the loss of A.C. power, which makes the primary side F&B ineffective, leading to the core meltdown within 2 h. The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) is used to calculate Human Error Probability (HEP) for the primary and secondary side F&B recoveries. Probabilistic Safety Assessment (PSA) reveals that the proposed recovery strategy reduces the Core Damage Frequency (CDF) by one order of magnitude.

Assessment of Human Reliability Under the Conditions of Uncertainty: SPAR-H Methodology Interpreted in Terms of Interval-Valued Probabilities

Abstract The well-known standardized plant analysis risk-human reliability (SPAR-H) methodology is widely used for analysis of human reliability in complex technological systems. It allows assessing the human error probability taking into account eight important groups of performance shaping factors. Application of this methodology to practical problems traditionally involves assumptions which are difficult to verify under the conditions of uncertainty. In particular, it introduces only two possible values of the nominal human error probabilities (for diagnosis and for actions) which do not cover the whole spectrum of the tasks within operator's activity. In addition, although the traditional methodology considers the probabilities of human errors as the random variables, it operates only on a single predefined type of distribution for these variables and does not deal with the real situations in which the type of distribution remains uncertain. The paper proposes modification to the classical approach to enable more adequate modeling of real situations with the lack of available information. The authors suggest usage of the interval-valued probability technique and of the expert judgment on the maximum probability density for actual probabilities of human errors. Such methodology allows obtaining generic results that are valid for the entire set of possible distributions (not only for one of them). The modified methodology gives possibility to derive final assessments of human reliability in interval form indicating “the best case” and “the worst case.” A few numerical examples illustrate the main stages of the suggested procedure.

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.

Application of a Standardized Plant Analysis Risk-human Reliability Method to Pipeline Inspection Gauge Operations

Application of a Standardized Plant Analysis Risk-human Reliability Method to Pipeline Inspection Gauge Operations

An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’

Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+.We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability.For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk.

Lessons learned from applying a new HRA method for the petroleum industry

Petro-HRA is a new human reliability analysis method developed for the petroleum industry. The human error quantification step in the Petro-HRA method was adapted from the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method, which was originally developed for nuclear applications. During the Petro-HRA project, emphasis was placed on evaluating and adapting the SPAR-H quantification process to make it more suitable to a petroleum context. In addition, since HRA is relatively new to the petroleum industry, emphasis was also placed on the development of guidance for how to perform qualitative analysis to inform and provide the necessary substantiation for human error quantification and human error reduction. Petro-HRA is thus considered to be a “complete” method. This paper describes the key differences between the petroleum and nuclear industries, which created the need for a new HRA method. An overview of the Petro-HRA method is provided, and we document our observations and experiences from application of this new method in case studies and in commercial practice.

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.

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.

Modeling Human Reliability in the Power Grid Environment: an Application of the SPAR-H Methodology

Cascading failures, such as the Northeast outage of 2003, can keep millions of people without power for extended periods of time. A combination of simultaneous occurrence of rare events in the physical grid, the human inability to apprehend and act on the real circumstances, and failures in the IT infrastructure result- ing in poor situation awareness may lead to power outages. The formation and propagation of cascading events is nonlinear. There is a precursor phase, in which some system recovery is possible and human oper- ators are able to act and recover failed lines, followed by an escalation phase, in which the number of failed lines increases rapidly, and by a cascading phase that corresponds to the blackout. Although stochastic power grid simulation is an appropriate tool to study cascading events, human intervention, which is mostly ignored in simulation, can nonetheless influence the recovery and the extension of the blackouts. In this pa- per we model the probability of human failure by applying a human reliability modeling approach, the, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H), to the analysis of failure scenari- os narrated by grid operators with different levels of expertise. The SPAR-H methodology is a simplified approach to human error quantification that accounts for individual performance-shaping factors that affect perception, processing and response to events in complex environments. To implement this approach, we started by interviewing system operators from New England and the South East, and took note of the narra- tives of the emergencies that happened during their shift. Next, we applied the SPAR-H methodology to each task and calculated dependency between the cognitive and action-based tasks. We then calculated the final failure probability and compared the empirical evaluation of error probability with what actually oc- curred. Using Monte Carlo methods and the SPAR-H equations, we finally estimated the HEP that can be readily integrated into the stochastic simulation of a physical power grid. With the intention to calculate the HEP specific to each cascading phase, we propose to increase the number of narrated events and classify each, according to a phase (precursor and escalation). We will then improve the model by calculating the frequency associated with each level of severity for each factor, and by creating a probability distribution for each factor, conditional to a specific phase.

Task complexity as a performance shaping factor: A review and recommendations in Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) adaption

This review is part of the PetroHRA project that aims at adapting human reliability analysis to the petroleum industry. The different elements that make up complexity are reviewed through a thematic analysis which identified 83 complexity elements that were categorized into 13 complexity categories. Six of these were found to be relevant and were included in the “Task Complexity” performance shaping factor which is presented in this paper. The description of “Task Complexity” includes levels and multipliers.

Human Error Data Collection and Comparison with Predictions by SPAR‐H

There is a scarcity of empirical data on human error for human reliability analysis (HRA). This situation can increase the variability and impair the validity of HRA outcomes in risk analysis. In this work, a microworld study was used to investigate the effects of performance shaping factors (PSFs) and their interrelationships and combined effects on the human error probability (HEP). The PSFs involved were task complexity, time availability, experience, and time pressure. The empirical data obtained were compared with predictions by the Standardized Plant Analysis Risk-Human Reliability Method (SPAR-H) and data from other sources. The comparison included three aspects: (1) HEP, (2) relative effects of the PSFs, and (3) error types. Results showed that the HEP decreased with experience and time availability levels. The significant relationship between task complexity and the HEP depended on time availability and experience, and time availability affected the HEP through time pressure. The empirical HEPs were higher than the HEPs predicted by SPAR-H under different PSF combinations, showing the tendency of SPAR-H to produce relatively optimistic results in our study. The relative effects of two PSFs (i.e., experience/training and stress/stressors) in SPAR-H agreed to some extent with those in our study. Several error types agreed well with those from operational experience and a database for nuclear power plants (NPPs).