scholarly journals A THERP/ATHEANA Analysis of the Latent Operator Error in Leaving EFW Valves Closed in the TMI-2 Accident

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Renato A. Fonseca ◽  
Antonio Carlos M. Alvim ◽  
Paulo Fernando F. Frutuoso e Melo ◽  
Marco Antonio B. Alvarenga
Keyword(s):  
Human Error ◽  
Feed Water ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Operator Error ◽  
Permanent Loss ◽  
Error Probabilities ◽  
Comprehensive Modeling ◽  
Accident Sequences ◽  
Prior Analysis

This paper aims at performing a human reliability analysis using THERP (Technique for Human Error Prediction) and ATHEANA (Technique for Human Error Analysis) to develop a qualitative and quantitative analysis of the latent operator error in leaving EFW (emergency feed-water) valves closed in the TMI-2 accident. The accident analysis has revealed a series of unsafe actions that resulted in permanent loss of the unit. The integration between THERP and ATHEANA is developed in a way such as to allow a better understanding of the influence of operational context on human errors. This integration provides also, as a result, an intermediate method with the following features: (1) it allows the analysis of the action arising from the plant operational context upon the operator (as in ATHEANA), (2) it determines, as a consequence from the prior analysis, the aspects that most influence the context, and (3) it allows the change of these aspects into factors that adjust human error probabilities (as in THERP). This integration provides a more realistic and comprehensive modeling of accident sequences by considering preaccidental and postaccidental contexts, which, in turn, can contribute to more realistic PSA (Probabilistic Safety Assessment) evaluations and decision making.

Analysis and Estimation of Human Errors From Major Accident Investigation Reports

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Caroline Morais ◽  
Raphael Moura ◽  
Michael Beer ◽  
Edoardo Patelli
Keyword(s):  
Human Error ◽  
Human Performance ◽  
Accident Investigation ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Near Misses ◽  
Performance Shaping Factors ◽  
Major Accident ◽  
Using Data ◽  
Error Probabilities

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.

scholarly journals Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

2021 ◽  
Vol 11 (2) ◽  
pp. 749
Author(s):  
Yaniel Torres ◽  
Sylvie Nadeau ◽  
Kurt Landau
Keyword(s):  
Human Error ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Manual Assembly ◽  
Human Errors ◽  
Assembly Instructions ◽  
Inspection Process ◽  
And Performance ◽  
Error Probabilities ◽  
Assembly Guidance

Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

Revisiting the FPSO São Mateus Accident From a Human Reliability Perspective Using Phoenix HRA Methodology

2020 ◽  
Author(s):  
Marilia A. Ramos ◽  
Alex Almeida ◽  
Marcelo R. Martins
Keyword(s):  
Human Error ◽  
Oil And Gas ◽  
Risk Estimation ◽  
Petroleum Industry ◽  
Oil And Gas Industry ◽  
Qualitative Assessment ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Gas Industry

Abstract Several incidents in the offshore oil and gas industry have human errors among core events in incident sequence. Nonetheless, human error probabilities are frequently neglected by offshore risk estimation. Human Reliability Analysis (HRA) allows human failures to be assessed both qualitatively and quantitatively. In the petroleum industry, HRA is usually applied using generic methods developed for other types of operation. Yet, those may not sufficiently represent the particularities of the oil and gas industry. Phoenix is a model-based HRA method, designed to address limitations of other HRA methods. Its qualitative framework consists of three layers of analysis composed by a Crew Response Tree, a human response model, and a causal model. This paper applies a version of Phoenix, the Phoenix for Petroleum Refining Operations (Phoenix-PRO), to perform a qualitative assessment of human errors in the CDSM explosion. The CDSM was a FPSO designed to produce natural gas and oil to Petrobras in Brazil. On 2015 an explosion occurred leading to nine fatalities. Analyses of this accident have indicated a strong contribution of human errors. In addition to the application of the method, this paper discusses its suitability for offshore operations HRA analyses.

UNCERTAINTY OF HUMAN ERROR AND FUZZY APPROACH TO HUMAN RELIABILITY ANALYSIS

2006 ◽  
Vol 14 (01) ◽  
pp. 111-129 ◽  
Author(s):  
B. J. KIM ◽  
RAM R. BISHU
Keyword(s):  
Reliability Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Critical Factor ◽  
Fuzzy Approach ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Fuzzy Logic Approach

Human error is regarded as a critical factor in catastrophic accidents such as disasters at nuclear power plants, air plane crashes, or derailed trains. Several taxonomies for human errors and methodologies for human reliability analysis (HRA) have been proposed in the literature. Generally, human errors have been modeled on the basis of probabilistic concepts with or without the consideration of cognitive aspects of human behaviors. Modeling of human errors through probabilistic approaches has shown a limitation on quantification of qualitative aspects of human errors and complexity of attributes from circumstances involved. The purpose of this paper is to investigate the methodologies for human reliability analysis and introduce a fuzzy logic approach to the evaluation of human interacting system's reliability. Fuzzy approach could be used to estimate human error effects under ambiguous interacting environments and assist in the design of error free work environments.

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

2021 ◽  
pp. 1748006X2098674
Author(s):  
Salvatore F Greco ◽  
Luca Podofillini ◽  
Vinh N Dang
Keyword(s):  
Error Probability ◽  
Human Error ◽  
Behavioral Patterns ◽  
Human Reliability Analysis ◽  
Bayesian Hierarchical ◽  
Performance Variability ◽  
Procedural Guidance ◽  
Error Probabilities ◽  
Task Types ◽  
Analysis Models

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

scholarly journals Fuzzy Logic in HEART and CREAM Methods to Assess Human Error and Find an Optimum Method Using a Hierarchical Fuzzy System: A Case Study in a Steel Factory

2022 ◽  
Author(s):  
REZIE BOROUN ◽  
YASER TAHMASBI BIRGANI ◽  
ZEINAB MOSAVIANASL ◽  
GHOLAM ABBAS SHIRALI
Keyword(s):  
Steel Industry ◽  
Fuzzy System ◽  
Human Error ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
System A ◽  
Technical Advances ◽  
Hierarchical Fuzzy System

Numerous studies have been conducted to assess the role of human errors in accidents in different industries. Human reliability analysis (HRA) has drawn a great deal of attention among safety engineers and risk assessment analyzers. Despite all technical advances and the development of processes, damaging and catastrophic accidents still happen in many industries. Human Error Assessment and Reduction Technique (HEART) and Cognitive Reliability and Error Analysis Method (CREAM) methods were compared with the hierarchical fuzzy system in a steel industry to investigate the human error. This study was carried out in a rolling unit of the steel industry, which has four control rooms, three shifts, and a total of 46 technicians and operators. After observing the work process, reviewing the documents, and interviewing each of the operators, the worksheets of each research method were completed. CREAM and HEART methods were defined in the hierarchical fuzzy system and the necessary rules were analyzed. The findings of the study indicated that CREAM was more successful than HEART in showing a better capability to capture task interactions and dependencies as well as logical estimation of the HEP in the plant studied. Given the nature of the tasks in the studied plant and interactions and dependencies among tasks, it seems that CREAM is a better method in comparison with the HEART method to identify errors and calculate the HEP.  

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.

Post-Accident Human Reliability Analysis and Control of Mine Hoisting System

2012 ◽  
Vol 616-618 ◽  
pp. 461-464
Author(s):  
Zhao Xia Liu ◽  
Lian Jun Chen
Keyword(s):  
Failure Probability ◽  
Human Error ◽  
Response Probability ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Cognitive Failure ◽  
Hoisting System ◽  
Accident Consequence ◽  
And Control

Using the method of THERPand HCR this paper studies post- accident human error events of mine hoisting system which reflects accident consequence seriousness and accident treatment urgency. It ascertains cognitive failure probability P1, non-response probability P2 and the failure probability P3, and quantities and appraises degree of human reliability. Finally this paper analyzes causes of hoisting accident human errors ,by which probability human error can be reduced to the lowest limit.

scholarly journals Incorporating Human Reliability Analysis to enhance Maintenance Audits: The Case of Rail Bogie Maintenance

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Sarbjeet Singh ◽  
Arnab Majumdar ◽  
Miltos Kyriakidis
Keyword(s):  
Human Error ◽  
Time Pressure ◽  
Fault Tree Analysis ◽  
Disc Brake ◽  
Human Reliability Analysis ◽  
Bogie Frame ◽  
Human Errors ◽  
Management Personnel ◽  
Assembly Unit ◽  
Railway Maintenance

Human errors occurring during railway maintenance activities can significantly reduce the availability of equipment. Identification of potential human errors, their causes and prediction of the associated probabilities are important stages in order to manage such errors. This paper investigates the probability of human error during the maintenance of railway bogies. A case study examines technicians performing maintenance on the disc brake assembly unit, wheel set, and bogie frame under various error producing conditions in a railway maintenance workshop in Luleå, Sweden. The Human Error Assessment and Reduction Technique (HEART) is employed to determine the probability of human error occurring during each of the maintenance tasks, while fault tree analysis is used to define the potential errors throughout the maintenance process. The probability of a technician committing an error during the maintenance of the disc brake assembly, wheel set, and bogie frame is found to be 0.20, 0.039 and 0.021 respectively, with the human error probability (HEP) for the entire bogie 0.24. Time pressure, ability to detect and perceive problems, over-riding information, the need to make decisions and mismatches between the operator and designer’s model turn out to be major contributors to human error. These findings can help maintenance management personnel to better understand the error producing conditions that may lead to errors and in turn serve as an input to modify policies and guidelines for railway maintenance tasks.

scholarly journals Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254861
Author(s):  
Yaju Wu ◽  
Kaili Xu ◽  
Ruojun Wang ◽  
Xiaohu Xu
Keyword(s):  
High Temperature ◽  
Bayesian Network ◽  
Molten Metal ◽  
Error Probability ◽  
Human Error ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Metallurgy Industry ◽  
Human Error Probability

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

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