A Study of Expert Judgment on Human Error Probability

1988 ◽  
Vol 32 (15) ◽  
pp. 954-957
Author(s):  
Bernhard Zimolong ◽  
Barbara Stolte

An experiment was conducted to derive empirically human error probabilities from a task performed under 12 different conditions. The task was to control a simulated flexible manufacturing scenario (FMS) under three Performance Shaping Factors (PSF): Incentive, workload and event frequency of breakdowns. Six experts with background in human factors assess the relative contribution of each PSF in affecting the likelihood of failure with the multi attribute decomposition technique. The conversion of the assessment values to probabilities was achieved by the use of an empirically derived calibration equation. Results indicate a poor match of empirical HEPs and their estimates and increase the doubts that subjective estimation is a solution to the missing data problem in reliability measurement.

Author(s):  
Victor G. Krymsky ◽  
Farit M. Akhmedzhanov

Abstract The well-known standardized plant analysis risk-human reliability (SPAR-H) methodology is widely used for analysis of human reliability in complex technological systems. It allows assessing the human error probability taking into account eight important groups of performance shaping factors. Application of this methodology to practical problems traditionally involves assumptions which are difficult to verify under the conditions of uncertainty. In particular, it introduces only two possible values of the nominal human error probabilities (for diagnosis and for actions) which do not cover the whole spectrum of the tasks within operator's activity. In addition, although the traditional methodology considers the probabilities of human errors as the random variables, it operates only on a single predefined type of distribution for these variables and does not deal with the real situations in which the type of distribution remains uncertain. The paper proposes modification to the classical approach to enable more adequate modeling of real situations with the lack of available information. The authors suggest usage of the interval-valued probability technique and of the expert judgment on the maximum probability density for actual probabilities of human errors. Such methodology allows obtaining generic results that are valid for the entire set of possible distributions (not only for one of them). The modified methodology gives possibility to derive final assessments of human reliability in interval form indicating “the best case” and “the worst case.” A few numerical examples illustrate the main stages of the suggested procedure.


2011 ◽  
Vol 97-98 ◽  
pp. 825-830 ◽  
Author(s):  
Yong Tao Xi ◽  
Chong Guo

Safety is the eternal theme in shipping industry. Research shows that human error is the main reason of maritime accidents. Therefore, it is very necessary to research marine human errors, to discuss the contexts which caused human errors and how the contexts effect human behavior. Based on the detailed investigation of human errors in collision avoidance behavior which is the most key mission in navigation and the Performance Shaping Factors (PSFs), human reliability of mariners in collision avoidance was analyzed by using the integration of APJE and SLIM. Result shows that this combined method is effective and can be used for the research of maritime human reliability.


Author(s):  
Caroline Morais ◽  
Raphael Moura ◽  
Michael Beer ◽  
Edoardo Patelli

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.


1981 ◽  
Vol 25 (1) ◽  
pp. 105-109
Author(s):  
A. Mohsen M. Metwally ◽  
Zeinab A. Sabri ◽  
S. Keith Adams ◽  
Abdo A. Husseiny

Two techniques useful for the simulation and analysis of human performance in tasks involving nuclear power plant operation, maintenance and testing are evaluated. The SAINT and THERP techniques are compared with respect to their relevance to conducting task analysis, estimation of human error probabilities and accounting for performance shaping factors in nuclear power plants. The results show that the SAINT is more flexible and has promising features for human engineering studies of complex systems when compared to the static THERP technique currently used in nuclear safety analysis.


Author(s):  
Brandon D. Richards

Aircraft engine maintenance errors recorded during commercial revenue operation (field operation) share common causes and outcomes as those documented in original equipment manufacturer (OEM) and maintenance repair and overhaul (MRO) facilities. Utilizing data from one or more one of these sources can increase the understanding of skill-based, decision and perceptual errors occurring during assembly, operation and maintenance. The null hypothesis in this descriptive, explanatory research is: the nominal human error probability of removing and replacing a specified line replaceable unit (LRU)/component in field operations and OEM/MRO is the same. Quantitative analysis will include probabilities of human error for specific processes in both field operations and MRO/OEM environments determined by the Human Error Assessment and Reduction Technique (HEART). Qualitative analysis will include the classification of past errors utilizing the Human Factors Classification and Analysis System (HFACS). Accounting for all available types of data enables more precise and appropriate corrective actions.


Author(s):  
Mashrura Musharraf ◽  
Faisal Khan ◽  
Brian Veitch ◽  
Scott MacKinnon ◽  
Syed Imtiaz

This paper presents a quantitative approach to human factors risk analysis during emergency conditions on an offshore petroleum facility located in a harsh environment. Due to the lack of human factors data for emergency conditions, most of the available human factors risk assessment methodologies are based on expert judgment techniques. Expert judgment is a valuable technique, however, it suffers from vagueness, subjectivity and incompleteness due to a lack of supporting empirical evidence. These weaknesses are often not accounted for in conventional human factors risk assessment. The available approaches also suffer from the unrealistic assumption of independence of the human performance shaping (HPS) factors and actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments and to account for the dependency among the HPS factors and actions. These outcomes are achieved by integrating Bayesian Networks with Fuzzy and Evidence theories to estimate human error probabilities during different phases of an emergency. To test the applicability of the approach, results are compared with an analytical approach. The study demonstrates that the proposed approach is effective in assessing human error probability, which in turn improves reliability and auditability of human factors risk assessment.


2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Danilo Taverna Martins Pereira de Abreu ◽  
Marcos Coelho Maturana ◽  
Enrique Andrés López Droguett ◽  
Marcelo Ramos Martins

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.


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