Methodology for the analysis and quantification of human error probability in manufacturing systems

HEP quantification strategy based on modified CREAM

Objective In this study, a hybrid human error assessment (HHEA) approach is proposed to determine the probability of aircraft maintenance technician (AMT) error occurring during aircraft repair. Background The human error assessment and reduction technique (HEART) is the first-generation empirical human reliability analysis (HRA) approach. Although this technique is used in several industries, AMTs’ errors have not been assessed with HEART and interactions of error-producing conditions (EPCs) have not been considered in calculating the probability of AMTs’ errors. Method HHEA is proposed to reduce the subjectivity of experts’ judgments by calculating assessed proportion of affect of each EPC on human error probability (HEP) integrating HEART and the analytic network process (ANP) method. The proposed model has been applied in the maintenance of the elevator, which is one of the most important primary control surfaces for the Cessna 172 Series aircraft type. Results The HEP values of 3 subtasks are found to be higher than the average HEP value. These subtasks have the highest probability of human error due to time pressure, poor environmental conditions, and being a repetitive task. Conclusion This study could guide other aircraft maintenance organizations using this integration of human reliability assessment approach for critical maintenance practices to enhance flight safety and minimize human error.

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

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

Plant-specific human reliability analysis (HRA) tailors human error probabilities (HEPs) to a facility's unique contexts. The diversity of digital human-machine interfaces and operational cultures necessitates methods grounded in empirical data rather than generic databases to ensure true plant-specificity. Advancing beyond prior fragmented data applications, this study establishes a systematic pipeline integrating the HuREX (Human Reliability data Extraction) database with the EMBRACE (EMpirical data-Based crew Reliability Assessment and Cognitive Error analysis) method. By sharing a unified task taxonomy, this framework directly translates plant-specific simulator data into coherent HEP estimates. In an application of this framework to the APR1400 plant, time-dependent failure probabilities were derived using Bayesian inference on 30 simulator performance records. Concurrently, nominal primitive error probabilities (NPEPs) were estimated via logistic regression from 44,585 task records. Due to current data scarcity, performance shaping factor (PSF) multipliers were determined through structured expert elicitation. This data-method integration provides a replicable foundation for generating context-sensitive HEPs in digitalized control rooms, highlighting both empirical strengths and current data limitations. • A plant-specific HRA approach is developed by integrating HuREX with EMBRACE. • HuREX data support the estimation of the time and cognitive failure probabilities. • The proposed approach is applied to estimate HEPs for the APR1400 plant. • Limitations such as reliance on expert judgment are discussed.

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.”

As manufacturers and technologies become more complicated, manufacturing errors such as machine failure and human error have also been considered more over the past. Since machines and humans are not error-proof, managing the machines and human errors is a significant challenge in manufacturing systems. There are numerous methods for investigating human errors, fatigue, and reliability that categorized under Human Reliability Analysis (HRA) methods. HRA methods use some qualitative factors named Performance Shaping Factors (PSFs) to estimate Human Error Probability (HEP). Since the PSFs can be considered as the acceleration factors in Accelerated Life Test (ALT). We developed a method for Accelerated Human Fatigue Test (AHFT) to calculate human fatigue, according to fatigue rate and other effective factors. The proposed method reduces the time and cost of human fatigue calculation. AHFT first extracts the important factors affecting human fatigue using Principal Component Analysis (PCA) and then uses the accelerated test to calculate the effect of PSFs on human fatigue. The proposed method has been applied to a real case, and the provided results show that human fatigue can be calculated more effectively using the proposed method.

Analyses of human reliability during manned spaceflight are crucial because human error can easily arise in the extreme environment of space and may pose a great potential risk to the mission. Although various approaches exist for human reliability analysis (HRA), all these approaches are based on human behavior on the ground. Thus, to appropriately analyze human reliability during spaceflight, this paper proposes a space‐based HRA method of quantifying the human error probability (HEP) for space missions. Instead of ground‐based performance shaping factors (PSFs), this study addresses PSFs specific to the space environment, and a corresponding evaluation system is integrated into the proposed approach to fully consider space mission characteristics. A Bayesian network is constructed based on the cognitive reliability and error analysis method (CREAM) to model these space‐based PSFs and their dependencies. By incorporating the Bayesian network, the proposed approach transforms the HEP estimation procedure into a probabilistic calculation, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex space environment. More importantly, by acquiring more information, the HEP estimates can be dynamically updated by means of this probabilistic calculation. By studying 2 examples and evaluating the HEPs for an International Space Station ingress procedure, the feasibility and superiority of the developed approach are validated both mathematically and in a practical scenario.

Human reliability analysis (HRA) examines human errors or human failure events (HFEs) and their probabilities (Human Error Probabilities, HEPs) under specific task conditions using various models. Given the scarcity of empirical data, expert judgments remain vital in the HRA community. Despite prior efforts to model the expert elicitation process using Bayesian methods, there is still a lack of comprehensive understanding of the overall process and its key components in the Bayesian context, which undermines the effectiveness and applicability of these models. To address this gap, this study starts by investigating the sources of uncertainties in various HRA constructs and elements of the expert elicitation process, aiming to form rigorous definitions in probabilistic terms. Furthermore, practical elicitation approaches are modeled and common misunderstandings about Bayesian modeling in past HRA research are examined and addressed. Based on this foundation, a Bayesian model is developed to integrate the different elements with the HRA constructs, and the probabilistic relationships between them are analyzed and elucidated. Two direct applications of the model are also provided to demonstrate the model’s practical applicability, highlighting its strengths and limitations. The proposed model not only offers a formal approach to rigorously incorporating expert judgments into HRA but also enhances researchers’ and practitioners’ understanding of the uncertainties related to the expert elicitation process, thereby providing a solid theoretical foundation for the development of future Bayesian models.

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

Preparation for a sea voyage is one of the fundamental aspects of navigation. Several complexities are involved during the preparation of the ship for navigation due to the nature of maritime work. At this point, analysing human-related error is of paramount importance to ensure the safety of the ship and the crew. This paper describes the principles of a methodology, namely fuzzy-based shipboard operation human reliability analysis (SOHRA), to quantitatively perform human error assessment through procedures of preparing the ship for navigation. While the SOHRA (a marine-specific HRA approach) quantifies human error, the fuzzy logic deals with ambiguity and vagueness in the human error detection problem. The findings show that the total HEP (Human error probability) is found 1.49E-01 for preparing the ship for navigation. Consequently, the paper provides practical contributions to shore-based safety professionals, ship managers, and masters of the ship since it performs a systematic human reliability assessment and enhances safety control levels in the operational aspect.

As part of an overall effort to upgrade and streamline methodologies for safety analyses of nonreactor nuclear facilities at the Savannah River Site (SRS), a human error data base has been developed and is presented in this report. The data base fulfills several needs of risk analysts supporting safety analysis report (SAR) development. First, it provides a single source for probabilities or rates for a wide variety of human errors associated with the SRS nonreactor nuclear facilities. Second, it provides a documented basis for human error probabilities or rates. And finally, it provides actual SRS-specific human error data to support many of the error probabilities or rates. Use of a single, documented reference source for human errors, supported by SRS-specific human error data, will improve the consistency and accuracy of human error modeling by SRS risk analysts. It is envisioned that SRS risk analysts will use this report as both a guide to identifying the types of human errors that may need to be included in risk models such as fault and event trees, and as a source for human error probabilities or rates. For each human error in this report, ffime different mean probabilities or rates are presented to cover a wide range of conditions and influencing factors. The ask analysts must decide which mean value is most appropriate for each particular application. If other types of human errors are needed for the risk models, the analyst must use other sources. Finally, if human enors are dominant in the quantified risk models (based on the values obtained fmm this report), then it may be appropriate to perform detailed human reliability analyses (HRAS) for the dominant events. This document does not provide guidance for such refined HRAS; in such cases experienced human reliability analysts should be involved.

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).

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.