A Novel Dependence Assessment Method With Comparative Linguistic Expression and Hybrid Cloud Model in Human Reliability Analysis

Objectives. A necessity for this study was felt in the catalyst replacement process as a maintenance operation, because some fatal incidents have occurred due to human error in process industries during catalyst replacement operation. Identification and evaluation of human error is essential in predicting and reducing accidents in maintenance operation. Methods. The human error assessment and reduction technique (HEART) as a human reliability analysis was applied in this study. Because the HEART method uses expert judgment, it is possible to make a bias in the assessment. Fuzzification and aggregation opinions of a heterogeneous expert group in a trapezoidal fuzzy set were used to mitigate this deficiency. Results. Evaluation results showed that the subtask ‘Entering the reactor’ with human error probability (HEP) of 9.2E–1 and the subtask ‘Reduce temperature while reducing feed’ with HEP 1.3E–3 had the highest and lowest chance of human error, respectively. Conclusions. In order to reduce the likelihood of human error, it is necessary to take appropriate actions based on the task-specific error producing condition (EPC).

An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control

Chapter 5 - Human Reliability Analysis

Dependency definitions for quantitative human reliability analysis

The human errors are considered as the major contributor to the marine accidental events. The implementation of autonomous ship (AS) in the maritime industry, might be considered as a future alternative to reduce these human errors. However, this type of ship will create new vulnerabilities and paths to failure even as it provides benefits on other scores. When the AS is in the port, a maintenance staff embarks on board, to carry out the scheduled routine maintenance or requested repairs. Mistakes made by the maintenance staff may have fatal consequences on the safety of the AS. The wrong way of sea chest strainer cleanness is one of the causals that has contributed to several engine room water ingresses, causing a complete damage of the engine room equipment of many conventional ships. In this paper, we analyze and model this event sequence from a human perspective. Technical methodologies i.e. human reliability analysis (HRA) event tree is used for the identification of the vulnerabilities of the task procedure and the technique for human error rate prediction (THERP) is used for the quantification of these human errors probability. Based on this analysis, error barriers and error recovery mechanisms are proposed to prevent its consequences.

In the civil aviation sector, human factors is the primary cause of many safety incidents. Aircraft flying, maintenance, and operations are the major tasks that are heavily dependent on professionals; thus, they are subject to human error probability. Human reliability analysis (HRA), which can evaluate human state and managing risk, has been developed over the years to identify, predict, and reduce human failures throughout aircraft operating procedures. Different generations of HRA tools have been developed to quantify the risks that are associated with safety accidents, including such as the Human Error Assessment and Reduction Technique, Technique for Human Error Prediction, Standardized Plant Analysis Risk Human Reliability Analysis, Cognitive Reliability and Error Analysis Methods, and Bayesian Network (BN). However, little is known about how these approaches are applied in aviation safety. This review aimed to systematically examine the current status of research on HRA in aviation accidents. A total of 13 studies were included and encompassed the studies of the first, second, and third generalizations of HRA alone or in combination with other methods (e.g., Improved Analytics Hierarchy Process, Functional Resonance Analysis Methods, Human Factor Analysis and Classification System, and Fault Tree Analysis). Results revealed that the third-generation HRA with BN was frequently used, showing great application potential for flight safety risk prevention and reduction. In the future, testing other third-generation HRA models driven by data in the field of airworthiness is necessary.

A bibliometric analysis of human reliability research

As a high-tech industry, civil aviation industry should focus on technology, but accident reports show that at least 3/4 of the accidents are caused by human error. Therefore, in ATM system, human error is a potential weak link. We must take measures to minimize human error and its impact, and optimize the quality of human operation for error detection and recovery. The ultimate goal of the research on human errors in air traffic control is to take protective measures and means against human unsafe behaviors, so the identification and analysis of human errors in air traffic control is very important.KeywordsAir traffic controlHuman errorKnowledge representation frameworkOntologyRule reasoning

Human reliability analysis (HRA) has drawn increasing attention from both academic and industry sectors to proactively enhance system safety in recent decades. HRA mainly focuses on identifying, quantifying, modeling, and preventing human error which is recognized as the most complicated and leading cause in major accidents occurring. However, HRA practitioners have often experienced several serious issues in modeling human behavior owing to rare quantitative data, great uncertainty, and considerable complexity of human behavior. In recent years, fuzzy set theory (FST) has been substantially employed to relax some important challenges in numerous domains from healthcare to nuclear power plants. However, few academic attempts have been made to demonstrate how, and to what extent, HRA has been improved through the FST perspective. Accordingly, this chapter reviews state-of-the-art scientific research to reveal the applications, importance, and contributions of FST onto HRA and its related concerns. It explains the abovementioned aspects by (a) predicting human error probability, (b) quantifying influence of performance shaping factors in human performance, (c) modeling intra-dependency among these factors, (d) incorporating human error into probabilistic safety and risk analysis, (e) modeling human behavior and finally, (f) characterizing the uncertainty analysis in HRA by incorporating fuzzy set theory. This chapter offers useful insights into main challenges, gaps, and demands in HRA from both academic and industrial perspectives considering the FST point of view.

The traditional approach to the study of human factors in the maritime field involves the analysis of accidents without considering human factors reliability analysis. The main approach being use to analyze human errors are statistical approach and probability theory approach. Another suitable approach to the study of human factors in the maritime industry is the quasi-experimental field study where variations in performance (for example attention) can be observed as a function of natural variations in performance shaping factors. This paper analyzes result of modeling human error and human reliability emanating from the use of technology on board ship navigation in coastal water area by using qualitative and quantitative tools. Accident reports from marine department are used as empirical material for quantitative analysis. The literature on safety is based on common themes of accidents, the influence of human error resulting from technology usage design, accident report from MAIB and interventions information are use for qualitative assessment. Human reliability assessment involves analysis of accident in waterways, emanating from human-technology factors interface. This paper report an enhancement requirement of the methodological issues with previous research study, monitoring and deduce recommendations for technology modification of the human factors necessary to improve maritime safety performance. The result presented can contribute to rule making, and safety management leading for development of guideline and standards for human reliability risk management for ship navigating within inland and coastal waters.

This paper introduces a taxonomy and accompanying database designed to bridge previous efforts at human reliability analysis (HRA) and human factors design. Previous HRA database efforts have focused primarily on incident analyses related to the safe operation of nuclear power plants. Previous human factors design standards have largely omitted a thorough investigation of the root causes of human error and their prevention through optimal design. The taxonomy introduced in this paper redresses these shortcomings in three ways: (1) It targets a domain outside the traditional realm of nuclear power plant operations. Specifically, it focuses on information that would be informative to human performance in extreme environments. (2) The taxonomy includes specific fields for incorporating completed HRAs, incident reports, and psychological research using human subjects or simulation. (3) The information captured through the taxonomy is applicable to both HRA and system design. The information format incorporates quantitative information appropriate to HRA such as human error probabilities and qualitative information appropriate to system design such as overall “lessons learned.”

This research aimed to identify and evaluate human errors in an industrial control room using the SHERPA (Systematic Human Error Reduction and Prediction Approach) method. The statistical population consisted of control room operators from Safola‐Behshahr Industrial Company, with data gathered through interviews with 18 expert employees. Data collection tools included field observations, checklists, and a custom questionnaire, validated by experts. The SHERPA method was utilized for data analysis. The findings revealed that energy unit operators had 26 main tasks and 47 subtasks. The analysis identified 135 errors, of which 48% were deemed unacceptable. Moreover, 23 errors were categorized as high‐risk, with the most critical issues including: absence of the operator in the control room, improper actions to control boiler pressure, mistiming or forgetting to adjust the control valve, lack of information exchange, and failure to control routes and dampers. Other critical errors included late ignition or failure to verify the controller's operation. Among the error types, action/functional errors had the highest occurrence (68%), while retrieval and communication errors were the least frequent (3%). The prioritized errors were used to generate control measures to reduce their frequency. The results suggest that implementing control measures can significantly reduce the risk of human errors in control room operations.

New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network

To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.

A comparison of the quantification aspects of human reliability analysis methods in nuclear power plants