Plant Analysis Risk-Human Reliability Research Articles

Enhancing Human Reliability Prediction in Smart Tower Crane Interfaces: A Refined Approach Using Simplified Plant Analysis Risk–Human Reliability Assessment and the Decision Making Trial and Evaluation Laboratory–Analytic Network Process

With the advent of Industry 4.0, the prevalence of tower cranes equipped with hook visualization is increasing. However, the introduction of new interface management tasks has led to novel patterns of human errors for operators. The Simplified Plant Analysis Risk–Human Reliability Assessment (SPAR-H) method has emerged as a relevant approach for the prediction of human reliability in smart construction tower crane operations. However, the current SPAR-H method is only partially applicable and does not fully meet the requirements of this study. Initially, a text mining approach (TF-IDF-TruncatedSVD-ComplementNB) was employed to identify operator error-specific terms in tower crane operations. These terms were then correlated with the eight Performance Shaping Factors (PSFs) of the SPAR-H method, and corresponding failure modes and potential causes were determined from the literature. This ensured a more objective selection of influencing factors and PSFs during the stratification process, which was validated through questionnaire surveys. Furthermore, standards for SPAR-H PSF levels were established based on the characteristics of tower crane operators. Given the inherent complexity of relationships among SPAR-H PSFs, the DEMATEL-ANP method was applied. This involved analyzing logical interactions and causal relationships between first-level and second-level indicators of PSFs, obtaining weights, and integrating these with the SPAR-H method to determine human reliability. Finally, an analysis and validation were conducted using a case study of an accident involving a smart construction tower crane, confirming the subsequent reliability of operator actions. The result of the accident case study yielded a reliability measure of 4.2 × 10−5. These findings indicate that the evaluation process of this method aligns with scenarios encountered in smart construction tower crane operations.

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.

Applying evidential reasoning extended SPAR-H modelling to analyse human reliability on crude oil tanker cargo operation

Human reliability analysis is essential to predict ship crew performance at the operation level for critical shipboard operations which pose a high risk. Specifically, the crew who are working on tanker ships has to be extra vigilant against the hazardous nature of the cargo. This paper performs a detailed human reliability analysis step by step for cargo discharging operations in crude oil tankers. To achieve this purpose, a Standardised Plant Analysis Risk Human Reliability Analysis (SPAR-H) extended by the Evidential Reasoning (ER) approach is used to systematically predict the probability of human errors. While SPAR-H provides a robust tool to calculate human error probability simultaneously, the ER fuses raters’ opinions in decision-making. The finding of the research shows that the overall human reliability is found at 6.52E-01 for cargo discharging operations in crude oil tankers. Besides its practical contributions to maritime crude oil transportation, the proposed approach presents how a comprehensive insight into human reliability can be gained.

Safety Analysis of Offshore Wells Plugging and Abandonment Process with Riserless Well Intervention System Using a DBN based Comprehensive Method

The Riserless Well Intervention (RLWI) system that performs complex offshore oil well Plugging and Abandonment (P&A) operations is a typical Multi-Mission Phased-Mission System (MM-PMS), which requires multiple missions to be completed within a phase. P&A processes involve complex operations and equipment that can contaminate local marine ecosystems if they fail. Therefore, it is necessary to evaluate the reliability of the RLWI system. This paper proposes a dynamic reliability evaluation model for analyzing the RLWI MM-PMS. The GO model of the phase operation process and the Fault Tree (FT) model used to analyze the failure of each mission were established, and a Dynamic Bayesian Network (DBN) model based on the GO model and the FT model was developed for reliability evaluation. The established model can analyze the changes in the reliability of the RLWI MM-PMS more comprehensively, and can also clarify the importance of different missions and different system components. In addition, considering the impact of the marine environment on operators, the Standardized Plant Analysis Risk-Human (SPAR-H) reliability analysis is used for quantification. These findings can guide the improvement of the reliability of the RLWI system and the success rate of P&A operations.

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.

Application of a SPAR-H based framework to assess human reliability during emergency response drill for man overboard on ships

Emergency preparedness is of paramount importance in successful emergency responses at sea. Therefore, emergency drills are regularly conducted to maintain acceptable levels of emergency preparedness. However, it needs to be considered that emergency drill operations themselves include significant risks, and there is no evidence that these risks are appropriately considered when planning emergency drill operations. Human error is one of the main contributors of accidents during emergency drill procedures. The main question posed is how overall risk, including human errors, during an emergency drill can be correctly evaluated. This paper introduces a new hybrid approach based on the Standardised Plant Analysis Risk Human Reliability Analysis (SPAR-H) method with a fuzzy multiple attributive group decision-making method. The method provides a framework for evaluating specific scenarios associated with human errors and identifies contributors that affect human performance. Estimated human errors are utilised to assess human reliability using a new approach based on a system reliability block diagram. The rescue boat drill procedure for a man overboard is selected to illustrate the method. The findings of this research show each human error probability and its contributing factors per task. As a result, overall reliability of 6.06E-01 was obtained for rescue boat drill operation.

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.

Quantification of the impact of factors affecting the technical performance of operating room personnel: Expert judgment approach.

Incorrect assessment of the performance shaping factors (PSFs), especially in health care systems, may lead to irreversible consequences such as death. The current study aimed to adapt PSFs taxonomy given in standardized plant analysis risk human reliability analysis (SPAR-H) technique to surgical context. PSFs taxonomy of the SPAR-H technique was revised, and more contextspecific definitions to surgical context were suggested. Step-wise weight assessment ratio analysis (SWARA) and eleven-digit numerical scale were used for quantification of the weight and negative influence rate of the PSFs during surgical processes, respectively. Nine PSFs were proposed following the SPAR-H taxonomy. The order of the importance of the PSFs was quietly conflicting in terms of weight and negative influence rate. From the perspective of experts, fatigue and threat stress were assessed as the most important PSFs in terms of both weight and negative influence rate. Current study has offered a domain-based understanding of the PSFs, particularly in the surgical context. Results of this study could be used to evaluate human error behaviors during the performance of tasks in the operating room. Moreover, these results could be used for improving patient safety programs in health care systems.

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.

Use of Bayesian networks and improved SPAR-H for quantitative analysis of human reliability during severe accidents mitigation process in nuclear power plant

ABSTRACT Human reliability analysis (HRA) for severe accidents is an important component of a Level 2 probabilistic risk assessment (PRA) for nuclear power plants. In this study, a Bayesian network (BN) is used to construct a qualitative causal model of human error during severe accidents by identifying the main cognitive functions (MCFs), crew failure modes (CFMs), and performance influencing factors (PIFs) for emergency personnel implementing severe accident management guidelines (SAMGs) after core damage. This qualitative model is used to develop a quantitative HRA method for severe accidents in nuclear power plants by improving the definition of performance-shaping factors (PSFs), levels, and multiplier criteria of the standardized plant analysis risk human reliability analysis (SPAR-H) method and applying BNs to quantify the failure probability of human performance. The usability of the proposed method is verified using a case study of emergency personnel following SAMGs to depressurize a reactor cooling system.

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.

Quantitative risk analysis of offshore well blowout using bayesian network

Blowout is the most feared and undesired accident during offshore drilling. It is inevitable, but the risk can be maintained to be below the acceptable criteria with effective strategies devised by risk analysis. An application of Bayesian networks (BN) for quantitative risk analysis on offshore blowouts was presented. First, we analyzed the SINTEF offshore well blowout data. 95% of blowout occurred in drilling, completion and workover during offshore drilling. Second, BN was applied to conduct risk analysis of offshore blowout. Based on these data, BN models were built. The prior probabilities with statistical probability method were calculated. The posterior probabilities during blowout were calculated using GeNIe software. The principal risk factors were identified by comparing them with prior probabilities. Shallow gas and abnormal high pressure were the principal risk factors of primary well control failure. Poor cementing and blowout preventer (BOP) failure were that of secondary well control failure. BOP failure is one of the main reason for blowout. Then, the risks of subsea and surface BOP failure were analyzed, combining with BN and Standardized Plant Analysis Risk Human Reliability Analysis Method. According to ExproSoft BOP failure data, the posterior probabilities with the concerning of component failure and human error were calculated. The principal factors were identified. This method provides greater value than the previous models since it can consider the complicated characteristics of geological condition, the whole offshore drilling, completion and workover technologies and operations, surface and subsea BOP common cause failures.

Systematic Human Reliability Analysis (SHRA): A New Approach to Evaluate Human Error Probability (HEP) in a Nuclear Plant

Emergency management in industrial plants is a fundamental issue to ensure the safety of operators. The emergency management analyses two fundamental aspects: the system reliability and the human reliability. System reliability is the capability of ensuring the functional properties within a variability of work conditions, considering the possible deviations due to unexpected events. However, system reliability is strongly related to the reliability of its weakest component. The complexity of the processes could generate incidental situations and the worker appears (human reliability) to be the weakest part of the whole system. The complexity of systems influences operator’s ability to take decisions during emergencies. The aim of the present research is to develop a new approach to evaluate human error probability (HEP), called Systematic Human Reliability Analysis (SHRA). The proposed approach considers internal and external factors that affect operator’s ability. The new approach is based on Nuclear Action Reliability Assessment (NARA), Simplified Plant Analysis Risk Human Reliability (SPAR-H) and on the Performance Shaping Factors (PSFs) relationship. The present paper analysed some shortcomings related to literature approaches, especially the limitations of the working time. We estimated HEP, after 8 hours (work standard) during emergency conditions. The correlations between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios emergencies. SHRA can be used to estimate human reliability during emergencies. SHRA has been applied in a nuclear accident scenario, considering 24 hours of working time. The SHRA results highlight the most important internal and external factors that affect operator’s ability.

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.