Using Simulator Data to Facilitate Human Reliability Analysis

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Mashrura Musharraf ◽  
Allison Moyle ◽  
Faisal Khan ◽  
Brian Veitch
Keyword(s):  
Reliability Analysis ◽  
Bayesian Methods ◽  
Human Error ◽  
Human Performance ◽  
Objective Assessment ◽  
Performance Data ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Emergency Situations ◽  
Emergency Scenarios

Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained noninformative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment are used to update the priors and obtain informed posteriors. Use of the informed posteriors enables better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations

2018 ◽  
Author(s):  
Mashrura Musharraf ◽  
Allison Moyle ◽  
Faisal Khan ◽  
Brian Veitch
Keyword(s):  
Reliability Analysis ◽  
Bayesian Methods ◽  
Human Error ◽  
Human Performance ◽  
Objective Assessment ◽  
Performance Data ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Emergency Situations ◽  
Emergency Scenarios

Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained non-informative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment is used to update the priors and obtain informed posteriors. Use of the informed posteriors enable better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model

2012 ◽  
Vol 226 (4) ◽  
pp. 361-379 ◽  
Author(s):  
Katrina M Groth ◽  
Ali Mosleh
Keyword(s):  
Reliability Analysis ◽  
Influencing Factors ◽  
Empirical Data ◽  
Human Performance ◽  
Theoretical Models ◽  
Performance Data ◽  
Human Reliability ◽  
Independent Research ◽  
Human Reliability Analysis ◽  
Proposed Model

Within the probabilistic risk assessment community, there is a widely acknowledged need to improve the scientific basis of human reliability analysis (HRA). This has resulted in a number of independent research efforts to gather empirical data to validate HRA methods and a number of independent research efforts to improve theoretical models of human performance used in HRA. This paper introduces a methodology for carefully combining multiple sources of empirical data with validated theoretical models to enhance both qualitative and quantitative HRA applications. The methodology uses a comprehensive set of performance influencing factors to combine data from different sources. Further, the paper describes how to use data to gather insights into the relationships among performance influencing factors and to build a quantitative HRA causal model.  To illustrate how the methodology is applied, we introduce the Bayesian network model that resulted from applying the methodology to two sources of human performance data from nuclear power plant operations. The proposed model is introduced to demonstrate how to develop causal insights from HRA data and how to incorporate these insights into a quantitative HRA model. The methodology in this paper provides a path forward for carefully incorporating emerging sources of human performance data into an improved HRA method. The proposed model is a starting point for the next generation of data-informed, theoretically-validated HRA methods.

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

Kerntechnik ◽  
2021 ◽  
Vol 86 (6) ◽  
pp. 470-477
Author(s):  
M. Farcasiu ◽  
C. Constantinescu
Keyword(s):  
Reliability Analysis ◽  
Error Rate ◽  
Error Detection ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Factor Engineering ◽  
Rate Prediction

Abstract This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.

Human Reliability Analysis of Ship Maneuvers in Harbor Areas

2019 ◽  
Author(s):  
Danilo T. M. P. Abreu ◽  
Marcos C. Maturana ◽  
Marcelo R. Martins ◽  
Siegberto R. Schenk
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Human Performance ◽  
Performance Model ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Ship Maneuvers ◽  
Open Sea ◽  
Expert’S Opinion

Abstract During a ship life cycle, one of the most critical phases in terms of safety refers to harbor maneuvers, which take place in restricted and congested waters, leading to higher collision and grounding risks in comparison to open sea navigation. In this scenario, a single accident may stop the harbor’s traffic as well as incur into patrimonial damage, environmental pollution, human casualties and reputation losses. In order to support the vessel’s captain during the maneuver, local experienced maritime pilots stay on board coordinating the ship navigation while in restricted waters. Because of their shorter relative duration, harbor maneuvers accidents are more probable to occur due to human errors — reinforced by the inherent surrounding difficulties —, rather than machinery failures, for instance. The human errors are object of study of the human reliability analysis (HRA). Aiming to assess the main factors contributing to human errors in pilot-assisted harbor ship maneuvers, this work proposes a Bayesian network model for HRA, supported by a prospective human performance model for quantification. Similar works focus mainly on open sea navigation and collision accidents, which do not reflect the strict conditions found on port areas. Additionally, most of the models are highly dependent on expert’s opinion for quantification. Therefore, the novelty of this work resides into two aspects: a) incorporation of harbor specific conditions for maritime navigation HRA, including the performance of ship’s crew and maritime pilots; and b) the use of a prospective human performance model as an alternative to expert’s opinion for quantification purposes. To illustrate the usage of the proposed methodology, this paper presents an analysis of the route keeping task along waterways, starting from the quantification of human error probabilities (HEP) and including the ranking of the main external factors that contribute to the HEP.

Human Reliability Analysis of Ship Maneuvers in Harbor Areas

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Danilo Taverna Martins Pereira de Abreu ◽  
Marcos Coelho Maturana ◽  
Enrique Andrés López Droguett ◽  
Marcelo Ramos Martins
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Human Performance ◽  
Performance Model ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Performance Shaping Factors ◽  
Ship Maneuvers ◽  
Critical Phases ◽  
Error Probabilities

Abstract During a ship life cycle, one of the most critical phases in terms of safety refers to harbor maneuvers, which take place in restricted and congested waters, leading to higher collision and grounding risks in comparison to open sea navigation. In this scenario, a single accident may stop the harbor's traffic as well as incur in patrimonial damage, environmental pollution, human casualties, and reputation losses. In order to support the vessel's captain during the maneuver, local experienced maritime pilots stay on board coordinating the ship navigation while in restricted waters. Aiming to assess the main factors contributing to human errors in pilot-assisted harbor ship maneuvers, this work proposes a Bayesian network model for human reliability analysis (HRA), supported by a prospective human performance model for quantification. The novelty of this work resides into two aspects: (a) incorporation of harbor specific conditions for maritime navigation HRA, including the performance of ship's crew and maritime pilots and (b) the use of a prospective human performance model as an alternative to expert's opinion for quantification purposes. To illustrate the usage of the proposed methodology, this paper presents an analysis of the route keeping task along waterways, starting from the quantification of human error probabilities (HEP) and including the ranking of the main performance shaping factors that contribute to the HEP.

scholarly journals A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

2019 ◽  
Vol 12 (1) ◽  
pp. 115 ◽  
Author(s):  
Chiara Franciosi ◽  
Valentina Di Pasquale ◽  
Raffaele Iannone ◽  
Salvatore Miranda
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Human Performance ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Main Research ◽  
Performance Shaping Factors ◽  
Research Areas ◽  
Industrial Maintenance ◽  
Maintenance Activities

Purpose: Human factors play an inevitable role in maintenance activities, and the occurrence of Human Errors (HEs) affects system reliability and safety, equipment performance and economic results. The high HE rate increased researchers’ attention towards Human Reliability Analysis (HRA) and HE assessment approaches. In these approaches, various environmental and individual factors influence the performance of maintenance operators affecting Human Error Probability (HEP) with a consequent variability in the success of intervention. However, a deep analysis of such factors in the maintenance field, often called Performance Shaping Factors (PSFs), is still missing. This has led the authors to systematically evaluate the literature on Human Error in Maintenance (HEM) and on the PSFs, in order to provide a shared PSF taxonomy.Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify and select peer-reviewed papers that provided evidence on the relationship between maintenance activities and human performance. The obtained results provided a wide overview in the field of interest, shedding light on three main research areas of investigation: methodologies for human error analysis in maintenance, performance shaping factors and maintenance error consequences. In particular, papers belonging to the area of PSFs were analysed in-depth in order to identify and classify the PSFs, with the aim of achieving the PSF taxonomy for maintenance activities. The effects of each PSF on human reliability were defined and detailed.Findings: A total of 63 studies were selected and then analysed through a systematic methodology. 46% of these studies presented a qualitative/quantitative assessment of PSFs through application in different maintenance activities. Starting from the findings of the aforementioned papers, a PSF taxonomy specific for maintenance activities was proposed. This taxonomy represents an important contribution for researchers and practitioners towards the improvement of HRA methods and their applications in industrial maintenance.Originality/value: The analysis outlines the relevance of considering HEM because different error types occur during the maintenance process with non-negligible effects on the system. Despite a growing interest in HE assessment in maintenance, a deep analysis of PSFs in this field and a shared PSF taxonomy are missing. This paper fills the gap in the literature with the creation of a PSF taxonomy in industrial maintenance. The proposed taxonomy is a valuable contribution for growing the awareness of researchers and practitioners about factors influencing maintainers’ performance.

scholarly journals Comparison of Methods for Dependency Determination between Human Failure Events within Human Reliability Analysis

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Marko Čepin
Keyword(s):  
Reliability Analysis ◽  
Safety Assessment ◽  
Human Error ◽  
Human Performance ◽  
Subjective Evaluation ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Probabilistic Safety Assessment ◽  
Error Probabilities ◽  
Probabilistic Safety

The human reliability analysis (HRA) is a highly subjective evaluation of human performance, which is an input for probabilistic safety assessment, which deals with many parameters of high uncertainty. The objective of this paper is to show that subjectivism can have a large impact on human reliability results and consequently on probabilistic safety assessment results and applications. The objective is to identify the key features, which may decrease subjectivity of human reliability analysis. Human reliability methods are compared with focus on dependency comparison between Institute Jožef Stefan human reliability analysis (IJS-HRA) and standardized plant analysis risk human reliability analysis (SPAR-H). Results show large differences in the calculated human error probabilities for the same events within the same probabilistic safety assessment, which are the consequence of subjectivity. The subjectivity can be reduced by development of more detailed guidelines for human reliability analysis with many practical examples for all steps of the process of evaluation of human performance.

Sign in / Sign up

Export Citation Format

Share Document