Human Error Analysis Research Articles

Risk assessment of probable human errors in the scaffold erection and dismantling procedure: a fuzzy approach

Objectives. Despite widespread use of scaffolds on construction sites, they are the most common causes of accidents, injuries and fatalities in this sector. About 80–90% of all occupational accidents occur as a result of human factors. On construction sites, proper erection/dismantling of scaffold is possibly the most essential factor in preventing accidents as it is one of the factors reported to be highly statistically significant in correlation to a high overall scaffold safety rating. Therefore, analysis of human errors (HEs) caused by scaffolders is important to be implemented. The main objective of this study was to evaluate possible HEs during scaffold erection and dismantling (SE&D). Methods. Stages involved were identified using hierarchical task analysis (HTA). Risk values of HEs were then evaluated using the fuzzy analytical hierarchy process (FAHP). Results. Our findings indicate that the highest and lowest risk values were related to the errors of ‘failure to reattach braces and connections prior to dismantling scaffolds’ and ‘insufficient preparation of the firm foundation’, respectively. Conclusion. This study formulates a method to estimate the risk of potential HEs in SE&D.

A human reliability analysis methodology based on an extended Phoenix method for severe accidents in nuclear power plants: Qualitative analysis framework

The mitigation of the Fukushima accident exposed the detrimental action of human errors once again. However, deficiencies in existing human reliability analysis (HRA) methods prevent the effective prediction and analysis of potential human errors during the mitigation of severe accidents following core damage. To address this problem, this study presents a qualitative analysis framework for emergency response personnel (ERP) reliability analysis under severe accident conditions. This framework is developed using the Phoenix HRA method in conjunction with analyzing the ERP response characteristics and human-related issues from previous accidents, literature surveys, drill observations and interviews, as well as an assessment of the applicability of existing HRA methods. The analysis framework includes the establishment of an ERP response tree and a human failure fault tree (FT), as well as the identification and compilation of ERP cognitive functions, human error modes (HEMs), performance-influencing factors (PIFs), and the construction of Bayesian belief networks, to provide methodological guidance for the prevention of human errors under severe accident conditions.

Human error analysis in furnace start-up operation using HEART under intuitionistic fuzzy environment

To address human error in system reliability, Human Reliability Analysis (HRA) is an essential issue. Human Error Assessment and Reduction Technique (HEART) as a rather straightforward technique for HRA has successfully been used in many areas to predict human error probability (HEP). However, knowledge acquisition of experts during assessed proportion of affect (APOA) calculation is subjected to vagueness and ambiguity. To overcome this challenge, in this paper Intuitionistic Fuzzy (IF) set due to their advantage to represent more fuzzy information than a classical fuzzy set adopted through APOA calculation. To demonstrate this hybrid approach short for, IF-HEART, the furnace start-up operation is handled, since analysis shows that most of explosions and losses occur during furnace start-ups operation. Further, a sensitivity analysis is carried out to approve the proposed integrated approach. In addition to its academic contribution, the results of the paper enable to improve the overall safety level of a furnace by taking into account potential human error.

ANALISIS HUMAN ERROR PADA OPERATOR ENGINE ROOM PT. ADEI PLANTATION & INDUSTRY DENGAN METODE HUMAN ERROR ASSESMENT AND REDUCTION TECHNIQUE (HEART)

PT. Adei Plantation & Industry is a plantation company that operates on plantation and palm oil industries. CPO production of palm oil is needed for consumption, which is run by power stations and for distribution (engine room) involving operators. Since january to june 2020, there is a sudden shutdown in the work of research for the analysis of human errors in the engine room using HEART method. The HEART method is used to evaluate the possibility of human error in all the work and to count the erros in various mays. Data management shows that there are 4 task operators that are common to the plant turbine engines and 6 task forces on the genertor. The highest HEP rating on the turbin engine was 0,6528 with the greatest error potential for task 3, the turbine engine operation. For the highest HEP value on the generator engine is 3,1759 with an error potential in task 5.

Analysis of Human Errors in a Commercial Aircraft Air Data System and their Influence on Air Safety

Analysis of Human Errors in a Commercial Aircraft Air Data System and their Influence on Air Safety

Human error analysis of the Montara well blowout

AbstractThe 2009 blowout from the Montara wellhead resulted the total loss of the West Atlas drilling rig and caused massive ecological damage in the Timor Sea of Australia. The incident preceded the 2010 Macondo Deepwater Horizon blowout and shared many parallels. An analysis is performed to assess the Montara incident and finds it stemmed from a series of human errors including, but not limited to, miscalculations, procedural errors, hasty decision making, and failure to follow the American Petroleum Institute Recommended Practices 75 on drilling mud circulation. Twelve specific errors were identified and classified into eight categories. The results show that 10 of the 12 errors were latent errors preceded by the 2004 relaxation of the Australian Government's off‐shore drilling industry regulations. Following industry guidelines, best practice procedures, and safe work practices are crucial to avoiding a well disaster and these were not a key aspect of the Montara wellhead operations; ultimately, safety was not a top priority.

Applications of the bias–variance decomposition to human forecasting

Forecasts are generated by both human experts and statistical models, and their forecast accuracy can be understood using error decompositions. However, the assumptions that underlie decompositions used in the analysis of human error differ substantially from those used in the analysis of models. The lens model, one of the most popular error decompositions for human errors, treats the beliefs of the human forecaster as fixed parameters to be estimated. Modern decompositions of model error treat the model as a random result from the process of fitting to noisy data. We highlight how these different approaches can be combined, expanding the application of the lens model to groups and opening up new perspectives on the study of human forecasting. We argue that treating human beliefs as the result of a process of learning from noisy data (even without specifying that process) can help to explain many documented phenomena in the world of forecasting such as: what kinds of environments human judgment will have difficulty with and what kinds they will be successful in; what conditions underlie the success of bootstrapping and aggregation of independent forecasts. Just as understanding statistical models as random variables has helped to improve the understanding of error in statistics and machines learning, we believe this framework will be able to help guide the literature on human judgment to a better understanding of error, its determinants and the mechanisms capable of improving forecasting accuracy.

Human Error Identification and Analysis for Shield Machine Operation Using an Adapted TRACEr Method

AbstractThis paper investigated shield machine operation (SMO) errors involved in shield tunneling construction accidents based on the Technique for the Retrospective and Predictive Analysis of Cog...

The analysis of human error in manufacturing: a key to improve production quality

A pesar del creciente nivel de automatización industrial, el ensamblaje manual continúa desempeñando un rol fundamental en diversos sectores de la manufactura. Sin embargo, las operaciones de tipo manual son susceptibles de errores humanos que ocasionan problemas de calidad y pérdidas económicas. El presente artículo se propone mostrar algunos métodos que permiten identificar diferentes tipos de errores y evaluar la influencia de factores que afectan el desempeño del trabajador. Se muestran, en particular, los métodos SHERPA y HEART. Igualmente se discute sobre la importancia de considerar la complejidad del ensamblaje por su negativo impacto en la carga cognitiva del trabajador lo que puede aumentar la probabilidad de error. En el artículo se emplean conceptos provenientes de la literatura especializada y se realiza una articulación de varias ramas del conocimiento tales como la ergonomía, la ingeniería industrial y la fiabilidad de sistema

Risk analysis of human error in interaction design by using a hybrid approach based on FMEA, SHERPA, and fuzzy TOPSIS

AbstractUser experience is the focus of interaction design, and designing for errors is crucial for improving user experience. One method of designing for errors is to identify human errors and then initiate corrective actions on high‐risk errors to reduce their adverse effects. In this study, we proposed a hybrid approach for risk analysis of human error concerning user experience of interactive systems. In this approach, systematic human error reduction and prediction approach (SHERPA) is first adopted to identify human error concerning user experience. Subsequently, failure mode and effect analysis (FMEA) is used to analyze the risk factors of the error, including occurrence, severity, and detection. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is then used to calculate the risk priority number to rank the errors. Finally, corrective actions for high‐risk errors are recommended. An in‐vehicle information system was used to demonstrate the proposed approach. The results indicated that the proposed approach can effectively analyze the risk of human error concerning user experience and be used as a universal reliability approach for improving user experience in interaction design.

Building Bridges

Free AccessBuilding BridgesMichaela Schwarz and Melanie ToddMichaela Schwarz European Association for Aviation Psychology (EAAP), Vienna, Austria Search for more papers by this author and Melanie Todd Australian Aviation Psychology Association (AAvPA), Melbourne, Australia Search for more papers by this authorPublished Online:April 02, 2020https://doi.org/10.1027/2192-0923/a000187PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreSince 1956 the European Association for Aviation Psychology (EAAP) has been promoting the study of psychology and the scientific pursuit of applied psychology and human factors in the field of Aviation. In 1992 the Australian Aviation Psychology Association (AAvPA) was founded to join forces to raise awareness of aviation psychology and human factors in Australia and the Asia-Pacific region. Dr Melanie Todd, at the time AAvPA Secretary and I had the pleasure to serve on the AAvPA Board from 2008 to 2010. Together with the rest of the AAvPA executive, we worked hard to bring Aviation Psychology and Human Factors down under closer to Europe and vice versa. Our main levers back then were promoting each other’s bi-annual conferences/symposia, training courses and facilitating a close network between EAAP and AAvPA members.Today AAvPA members enjoy the same benefits as EAAP members and EAAP members are welcome to participate at the AAvPA Symposium at special AAvPA rates. Together with our members we have achieved several milestones. The foundation of the new journal Aviation Psychology and Applied Human Factors (APAHF) as the official journal for EAAP and AAvPA in 2011 in cooperation with Hogrefe Publishing was certainly a high point.We are proud that since the first issue in January 2011 and with combined efforts of the editorial board, associate editors, authors, members, and our colleagues from Hogrefe Publishing, two issues could be published timely each year. The journal is an indispensable means to promoting scientific research, applied practices, book reviews, news and events in aviation psychology and human factors. We are happy to see the number of manuscript submissions and the quality of original (scientific) articles is increasing each year. At the same time we are pleased to see science applied in practice sometimes in close cooperation with industry partners. Further, it has always been a goal of both EAAP and AAvPA to support students in the field of aviation psychology and human factors. This journal, in addition to the symposia, also provides a mechanism for student papers to be published and emerging research to be highlighted.In our view it is important to understand the needs of our industry stakeholders when it comes to human performance and people issues. We would like to see the journal further develop as a medium to inform researchers and academics about industry news and help identify possible research areas, questions and problems. In return, academia can inform industry partners by addressing questions and problem areas through scientific studies delivering sound empirical evidence.In this spirit, we encourage both academia and industry partners in aviation to continue to build bridges to increase aviation safety between Europe, Australia, and beyond. We hope that our readers will see the great potential our scientific journal can bring to their daily operations and look forward to more submissions addressing current industry developments and trends.Michaela Schwarz and Melanie ToddEAAP and AAvPA Presidentspresident@eaap.net; president@aavpa.orgMichaela Schwarz is an aviation psychologist and human factors expert working towards improving human performance in aviation and rail. She received her doctoral degree in psychology from the University of Graz, Austria in 2016. Her main research focus and specialities are the assessment and improvement of safety – just culture and safety-relevant behaviour, the integration of human performance elements in safety management systems and the development and delivery of human factors training programs. Michaela has been a member of the EAAP Board for 10 years and EAAP President for 2 years.Melanie Todd is a Human Factors specialist, working across various high reliability industries. She received her doctoral degree in psychology in 2014, with a focus on pilot performance and licensing. Melanie has worked in both regulatory and investigative roles as a specialist in human factors, primarily in aviation with experience in rail and maritime investigations. Her specialist interest areas include accident and incident investigation, human error analysis, fatigue risk management, and training. Melanie has been a member of the AAvPA Executive Committee for over 15 years and AAvPA President for over 6 years.Michaela Schwarz, EAAP, Vienna, Austria, E-mail president@eaap.netFiguresReferencesRelatedDetails Volume 10Issue 1March 2020ISSN: 2192-0923eISSN: 2192-0931 Published onlineApril 2, 2020 InformationAviation Psychology and Applied Human Factors (2020), 10, pp. 1-2 https://doi.org/10.1027/2192-0923/a000187.© 2020Hogrefe PublishingPDF download

Using Dynamic Bayesian Belief Network for analysing well decommissioning failures and long-term monitoring of decommissioned wells

There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run is usually conducted in a manner such that the lowest-cost contractor is engaged to plug several wells tapping the same reservoir. Thus, this makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks (BBN) to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen. The sensitive analysis and backwards diagnostic analysis demonstrated that the results agree with a statistical study of 103 wells. This affirms that well failure probabilities are under-estimated if independent failure is assumed.

Comparison of four major industrial disasters from the perspective of human error factor

This paper presents the preliminary findings of a project still in progress at INCDPM regarding” Knowledge transfer partnership and research development in the assessment and prevention of occupational risks which may conduct to disaster”. After studying the major industrial disasters of our times, it become clear that even with technological advancement, human error is still the major cause of accidents and incidents. Analysis of human error and their role in accidents is an important part of developing systematic methods for reliability in the industry and risk prediction. To obtain data for predictive analysis is necessary to analyse accidents and incidents to identify its causes in terms of component failures and human errors. Therefore, a proper understanding of human factors in the workplace is an important aspect in the prevention of accidents, and human factors should be considered in any program to prevent those that are caused by human error. The comparison between four major industrial disasters (Chernobyl, Bhopal, Deepwater Horizon, Alpha Piper) was made using Human Factors Analysis and Classification System (HFACS), a modified version of “Swiss Cheese” model that describes the levels at which active failures and latent failures/conditions may occur within complex operations.

Analysis of Human Error in Healthcare Industry: an Indian Perspective

In healthcare sector, employing human knowledge, on a larger scale, in the decision making process in identifying the risk sources is very common, and often the human error in the process results in adverse effects of unrecognized risks. Hence, the impact of human error in employing the human knowledge remains a major problem. To minimize the human error a fuzzy based risk assessment method using Ranked Risk Breakdown Structure (RRBS) model is proposed in this work. The proposed method is able to rank the risk resources on the basis of a risk score generating mechanism based on the probability of occurrence of risk and outcome of the risk. By recognizing the high ranking risk resources, that is, the risk drivers at higher levels of seriousness/severity, this fuzzy based methodology nullify the existing common human errors. The proposed methodology is validated with the actual past data of one decade period, the occurrence of risk and its effect in a healthcare industry situated in an urban city of India. The results of the experimentation reveal that the proposed methodology can be successfully implemented in all other industries in healthcare sector to minimize the human error in risk recognition. The suggested model is helpful for industrial managers/practitioners to tackle risk factors related with complex works.

An Experimental Analysis Method of Visual Performance on the Error Factors of Digital Information Interface

As per global accident statistics, human error accounts for over 85% of accidents. Therefore, human error analysis of cognitive behavior of operators can be the key to solving information interface design problems of digital smart task monitoring interfaces. This paper proposes an analytical method based on psychological experiments introduced into task monitoring interfaces to study reactions to error factors. It uses psychological techniques to conduct experiments which evoke physiological reactions to various error factors under different sub-interfaces of the monitoring system and sub-task environments. The behavioral and eye tracking data demonstrate the association between the error factors and the information interface. Error factors arising in visual search are directly related to the layout of the task-interface, the information proximity–position, and the information features-volume. Our method opens up new approaches for design optimizations of visual information interfaces and introduces novel concepts for the introduction of interface design via error factor analysis.

A Qualitative Analysis of Human Error within the DIBH Procedure

A Qualitative Analysis of Human Error within the DIBH Procedure

Analysis and Estimation of Human Errors From Major Accident Investigation Reports

Abstract Risk analyses require proper consideration and quantification of the interaction between humans, organization, and technology in high-hazard industries. Quantitative human reliability analysis approaches require the estimation of human error probabilities (HEPs), often obtained from human performance data on different tasks in specific contexts (also known as performance shaping factors (PSFs)). Data on human errors are often collected from simulated scenarios, near-misses report systems, and experts with operational knowledge. However, these techniques usually miss the realistic context where human errors occur. The present research proposes a realistic and innovative approach for estimating HEPs using data from major accident investigation reports. The approach is based on Bayesian Networks used to model the relationship between performance shaping factors and human errors. The proposed methodology allows minimizing the expert judgment of HEPs, by using a strategy that is able to accommodate the possibility of having no information to represent some conditional dependencies within some variables. Therefore, the approach increases the transparency about the uncertainties of the human error probability estimations. The approach also allows identifying the most influential performance shaping factors, supporting assessors to recommend improvements or extra controls in risk assessments. Formal verification and validation processes are also presented.

Use of error management theory to quantify and characterize residents’ error recovery strategies

BackgroundTraditional checklist metrics for surgical performance can miss key intraoperative decisions that impact procedural outcomes. Error-based assessments may help identify important metrics for evaluating operative performance and resident readiness for independent practice. MethodsThis study utilized human factors error analysis and error management theory to investigate a previously collected video database of resident performance during a simulated laparoscopic ventral hernia (LVH) repair on a table-top simulator using standard laparoscopic tools and mesh. Errors were deconstructed and coded using a structured observation tool and video analysis software. Error detection events and error recovery events were categorized for each operative step of the ventral hernia repair. ResultsResidents made a total of 314 errors (M = 15.7, SD = 4.96). There were more technical errors (63%) than cognitive errors (37%) and more commission errors (69%) than omission errors (30%). Almost half (47%) of all errors went completely undetected by the residents for the entire LVH repair. Of the errors that residents attempted to recover (n = 136), 86.0% were successfully recovered. Technical errors were four times more likely to be successfully recovered than cognitive errors (p = .020). ConclusionsOur results revealed specific details regarding residents’ error management strategies and provides validity evidence for the use of human factors error frameworks in surgical performance assessments. Practice in simulation-based learning environments may improve resident decision-making and error management opportunities by providing a structured experience where errors are explicitly characterized and used for training and feedback. Error management training may play a major role in equipping residents and junior faculty with the skills required for independent, high-quality operative performance.

Using Rio-Paris Flight 447 Crash to Assess Human Error and Failure Propagation Analysis Early in Design

Abstract While a majority of system vulnerabilities such as performance losses and accidents are attributed to human errors, a closer inspection would reveal that often times the accumulation of unforeseen events that include both component failures and human errors contribute to such system failures. Human error and functional failure reasoning (HEFFR) is a framework to identify potential human errors, functional failures, and their propagation paths early in design so that systems can be designed to be less prone to vulnerabilities. In this paper, the application of HEFFR within the complex engineering system domain is demonstrated through the modeling of the Air France 447 crash. Then, the failure prediction algorithm is validated by comparing the outputs from HEFFR and what happened in the actual crash. Also, two additional fault scenarios are executed within HEFFR and in a commercially available flight simulator separately, and the outcomes are compared as a supplementary validation.

Application of NASA-TLX and SHERPA in Setting Up Fused Filament Fabrication Equipment

The use of new technologies in productive processes nowadays poses a challenge to the users, mainly because those technologies create them a necessity for learning and adaptation. This study focused on the assessment and analysis of mental workload and human error as experienced by users of Fused Filament Fabrication (FFF) printers, which is the most recent development in a series of technologies known as Additive Manufacturing (AM). Eight subjects were asked to complete the setting up of three commercial FFF equipment. Hierarchical Task Analysis (HTA) was used to describe the tasks. NASA-TLX method was used to assess the mental workload involved, and SHERPA method was applied to identify human errors while completing the task. The results of HTA show that leveling the printing bed is the most demanding activity on the subject's concentration. NASA-TLX shows that Mental Demand subscale had the highest average punctuation with 78.75 points, followed by Temporal Demand with an average of 61 points. As for SHERPA analysis, 59 human errors were identified in that particular task. In detail, 54.2% were action errors, 22.0% were selection errors, and 18.6% were checking errors. The combination of HTA, NASA-TLX and SHERPA techniques, permitted a complete analysis of the interaction between humans and this technology, opening the way to improvements in the design of FFT equipment which possibly might prevent future effects on user’s health.