Performance Shaping Factors Research Articles

Human risk recognition and prediction in manned submersible diving tasks driven by deep learning models

The complexity of human cognition is increased by the multiple and interactive information clusters brought about by the application of advanced intelligent information technologies. Especially in systems operating in extreme regions far from society, human errors are more pronounced than ever before. Prolonged social isolation, extreme weightless or overweight environments, stressful atmospheres, and lack of situational awareness are all added potential elements contributing to human risk. Although the development of human reliability analysis methods and their variants continues to mature, accurately predicting the potential risk of dynamic human behavior from sparse and discrete events remains a great challenge. We focus on deep learning computational architectures that are similar to the cognitive processes and mechanisms of the brain, and build neural networks that match the perceptual activation and memory cycling of the cognitive features of the brain. This study focuses on investigating the ability of the joint SNN-ITLSTM network to predict human error behavior and the clusters of performance shaping factors that effectively characterize the far-social nature. Combining the bionic properties of SNN and the temporal update mechanism of LSTM in the form of hierarchical events constitutes a computationally efficient network architecture. Our results show that the joint model proposed in this study has the performance to strengthen temporal influences and characterize cognitive features of the brain.

Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

Dynamic human reliability analysis method for nuclear power plant main control room based on SPAR-H and SD

Human reliability analysis (HRA) plays a vital role in probabilistic risk assessment (PRA) by quantifying the reliability and safety of complex technical systems through the estimation of human error probabilities (HEPs). Standardized plant analysis risk human (SPAR-H) reliability analysis is widely used for HRA; it adjusts the nominal HEP by assigning different multipliers to the performance shaping factors (PSFs). However, SPAR-H cannot capture the time-varying nature of HEPs; therefore, it cannot realistically respond to the accumulation and fluctuation of human risk factors across the mission. Hence, this study proposed a systematic method of dynamic HRA. It includes decision-making trial and evaluation laboratory-interpretative structural modeling (DEMATEL-ISM) to qualitatively and quantitatively analyze the hierarchical causality among the PSFs and their influence on each other based on their relative relationship in SPAR-H and construct hierarchical causality diagrams among the PSFs. Additionally, it uses a system dynamics method in conjunction with the analysis of time-dependent PSFs based on the hierarchical causality diagrams of PSFs to respond to the time-varying nature of HEPs. The simulation and rationale check results of the cases show that the proposed dynamic HRA method can realize a more realistic and dynamic estimation of HEPs.

A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving

Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

Evaluation of the incident management system of a bigger city during the winter flood 2023/24 in northern Germany: Organized (ir)responsibility in the pre-disaster phase

Incident management systems operate within complex and uncertain conditions during disasters. Therefore, researching their activities is challenging as there is no clear right or wrong. The winter 2023/2024 flood event in the city of Oldenburg, Germany, provided an opportunity to evaluate crisis management under high uncertainty, although no disaster has eventually occurred. Three stakeholder groups of the Incident Management System were examined through After-Action Reviews: The Emergency Operation Center (EOC), specialist advisors, and the Area Command (AC). Data on performance shaping factors (descriptions and judgements) were collected and analyzed using the Countenance Evaluation Model as a general framework in combination with a Systems-Thinking approach. The study aimed to identify variables within and between groups as well as factors in the environment that either hindered or facilitated work transactions. It emerged that the work organization in bodies of public administration is at odds with the uncertain, fast-paced, and problem-oriented structure of Incident Management System operations, causing stress and adding workload to employees. Additionally, the separation of strategic and tactical decision making led to coordination issues due to divergent time structures and task logics of EOC and AC. Furthermore, although specialist advisors played a crucial role in risk assessments and setting the direction of risk mitigation activities, it became clear that this person related resource is scarce and difficult to embed into the Incident Command System. A final finding is the importance of the larger Emergency Operation Center working environment. The spatial constraints and noise levels caused stress.

Identifying a probe to visualize the variability of operating teams for supporting the human reliability analysis of nuclear power plants: An explanatory study

Operating teams consisting of several team members still play a critical role in coping with off-normal conditions in socio-technical systems. Thus, various kinds of human reliability analysis methods have been suggested based on the consideration of diverse performance shaping factors that can affect the performance of team members. Unfortunately, since multiple performance shaping factors can vary across operating teams (i.e., crew-to-crew variability), it is crucial to figure out how to visualize this variability in a systematic way. In this regard, comparing the cultural characteristics of operating teams with their performance would be a good starting point. This study investigates how cultural characteristics can be correlated with the occurrence of unsafe acts based on empirical data collected from operating teams working in the main control room of Korean domestic nuclear power plants. The cultural characteristics of the operating teams were visualized using five Hofstede’s cultural indices and compared with the number of unsafe acts observed from simulated off-normal conditions. As a result, a statistically significant correlation is found between the occurrence of unsafe acts and one of the Hofstede’s indices. From this finding, it is expected that a relevant probe to scrutinize crew-to-crew variability could be soundly determined in future works.

Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM

The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system-based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results.

A data-driven Bayesian network of management and organizational factors for human reliability analysis in the process industry

According to historical statistical data, management and organizational factors (MOFs) contribute more to process accidents than technique factors. Under the umbrella of socio-tech system theory, human reliability analysis (HRA) has become a critical part of systemic probability risk analysis. In many HRA techniques, MOFs are among the performance shaping factors (PSFs). However, the interactions and causality of MOFs to human errors are still difficult to quantify and lack validation. To fill these gaps, a framework is proposed, considering data source selection, CBN construction algorithm comparison, and results validation. The case study employed the open access eMARS database as a data source. The optimized hybrid structure learning algorithm and Bayesian criteria parameter learning algorithm are employed to build a Causal Bayesian Network (CBN) of (MOFs) that lead to human error. The proposed kernel CBN is validated through prediction accuracy and sensitivity analysis. For theoretical contribution, the validated kernel BN could generally serve as the heart part of more specific CBNs as a basis for future works. For practical applications, an application shows the model's ability to quantify the contribution of MOFs to system reliability. The results show that human-machine interacting system reliability is most sensitive to organizational factors such as adequate training and procedures.

Estimating the Human Error Probability during Lifeboat Drills

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea.

An investigation of the effect of workload on ship engine room operators using fNIRS

This is an investigation into the Performance Shaping Factors (PSFs) associated with ship engine room operators. This study looks at the effect of workload. There is a large portion of human error associated with marine incidents. Human error may be considered as a result of additional supplementary tasks on top of an accustomed workload. The aim of this study is to evaluate the effect of workload on human performance. To achieve this, a TRANSAS simulator series 5000 was used to replicate a real scenario with the addition of workload as a PSF. Each participant engaged in a fault detection and correction task. 20 participants were used for the workload study; all 20 were trained for 3 h to use the engine room software interface. The participants then completed a 30-min ballasting task. During this interaction, 50% of the participants underwent a simulated scenario where the workload was increased. The other 50% were given a standard task. Functional near-infrared spectroscopy (fNIRS) was used to measure the participant's activation levels, more specifically from the dorsolateral prefrontal cortex (DLPFC) region of the cerebrum. The results showed an increase in activation of the DLPFC during each phase of the task, this trend was magnified by the addition of increased workload. The results are discussed with respect to human performance during varying workload. From the results of this study, a human classification performance model was developed. This model can be used by the maritime industry to better evaluate and understand human error causation.

A Method for Human Systems Integration Requirements within Model Based Systems Engineering

AbstractThe Department of Defense (DoD) employs broad human factors requirements across various applications, resulting in a universal application of the same standards to a multitude of DoD acquisition systems. In unconventional warfare, specifically within missions conducted by US Special Operations Command (USSOCOM), operators face intensified workloads and domain‐specific challenges that current human factors considerations do not adequately address. The objective of this paper aims to introduce a novel framework, the Relational and Technological Capstone (RTC), designed to expand existing Human Systems Integration (HSI) requirements. The objective is to enhance the consideration of human factors in USSOCOM missions by addressing the unique challenges posed by intensified workloads and domain‐specific ontologies. The RTC employs a methodology‐driven approach utilizing both architectural and parametric diagrams. It integrates with Model Based Systems Engineering (MBSE) to improve the design of human‐system interactions, incorporating a Special Operations Task List and Performance Shaping Factors (PSFs) into aggregated parametrics. The results of this paper demonstrate the efficacy of RTC within MBSE, showcasing its value through improved design processes and as a foundation for new programs. The containment tree format aids in developing USSOCOM MBSE and opens possibilities for automation tools. Continual use of RTC contributes to the maturity of MBSE models and diagrams, fostering the evolution of a federation‐of‐models and Program of Record standards. This not only benefits subsequent SOCOM programs and projects but also facilitates cross‐cooperation with other nations in optimizing special operation acquisitions. The ultimate goal is to center the RTC around the operator, ensuring compatibility and optimization across cooperative nations' special operation acquisitions.

Connecting pathogen transmission and healthcare worker cognition: a cognitive task analysis of infection prevention and control practices during simulated patient care

BackgroundRelatively little is known about the cognitive processes of healthcare workers that mediate between performance-shaping factors (eg, workload, time pressure) and adherence to infection prevention and control (IPC) practices. We...

Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor

Abstract The Human Error Probabilities (HEP) can be estimated using multipliers that correspond to the level of Performance Shaping Factors (PSFs) in the Human Reliability Analysis (HRA). This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. Markov Chain Monte Carlo (MCMC) and Bayesian Monte Carlo (BMC) are used as Bayesian inference methods based on Monte Carlo techniques. MCMC is utilized to obtain the posterior distribution of the multipliers. BMC is used for the estimation of the moments of the posterior distribution such as the mean and variance. The results obtained by MCMC and that by BMC well agree with the reference results. As a case study, the data assimilation was performed using the results of the simulator experiment of Halden reactor. The results show that the multiplier changes by the result of a particular scenario and HEP of another scenario that uses the same multiplier also changes by data assimilation. Also, in the case study, the correlation between multipliers is obtained by the data assimilation and the correlation contributes to the reduction of uncertainty of HEP.

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

AbstractHuman error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR‐H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry‐specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

Investigating the influence of human errors in master-pilot information exchange on maritime accident risk during pilotage

Pilotage-related errors have led to too many accidents in recent years, resulting in significant economic losses and disruptions in global logistics. Despite its importance, Master-Pilot Exchange (MPX) can be overlooked due to several factors. This study focuses on MPX, a standard protocol for information exchange between shipmasters and maritime pilots to ensure safe ship handling. Deficiencies in MPX procedures have been highlighted in various accident reports, emphasizing the need for improvement. This research aims to identify an optimal MPX process, evaluate potential shortcomings, assess their likelihood and severity, and analyse the human factors contributing to these deficiencies. The study utilizes a fuzzy Bayesian Network (BN) to model the MPX process and identify factors that may lead to its failure. It also employs the Success Likelihood Index Method (SLIM) approach to calculate the probability of human errors in the MPX process and identify the performance-shaping factors triggering them. The finding of the Bayesian method shows that the machinery problem and the navigational equipment problem are the two most significant root causes of MPX process failure. According to the SLIM findings, providing details of "the ship's contingency plan", and "navigational information" are the two most significant tasks for this process. Among the identified performance-shaping factors, the degree of teamwork and harmony, experience and knowledge appear to be the three most significant contributors with the highest human error probability (HEP) in mentioned tasks. This study represents a significant contribution to research on master-pilot interactions, addressing a critical aspect of maritime safety.

An information flow field model for human–machine interaction

PurposeAbout 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.Design/methodology/approachThe performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.FindingsThe results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.Originality/valueThe novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

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.

Human error probability evaluation based on reference task using intuitionistic fuzzy theory

Human Reliability Analysis (HRA) is a critical issue for addressing human error in system reliability. There are numerous tasks for which human factors-related data are not available, rendering expert knowledge the only basis for assessing such tasks. However, the knowledge obtained from experts is subject to ambiguity and vagueness, which affects the usability of the assessment results. To overcome this challenge, in this paper a reference task based HRA method is proposed and the intuitionistic fuzzy set (IFS) is adopted because of its advantage of being able to handle ambiguous information. Firstly, to analyze the human error probability (HEP) of the target task, a reference task-based human error analysis model is introduced. Two solutions are provided: calculating the performance shaping factors (PSFs) distance between the reference task and the target task and establishing a quantitative relationship between PSFs and HEP. Secondly, the PSFs evaluation and inference methods based on triangular intuitionistic fuzzy numbers (TIFNs) are developed. Finally, the effectiveness and consistency of the two solutions of TIFN-HRA are demonstrated through a spaceflight refueling mission analysis. The distances between the results of the two solutions and the expert linguistic are compared and both results have the shortest distance to “Low”. However, solution I is simpler and the result is clearer.