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

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.

Author(s):  
Marilia A. Ramos ◽  
Alex Almeida ◽  
Marcelo R. Martins

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.


Author(s):  
B. J. KIM ◽  
RAM R. BISHU

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.


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.


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

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.


Author(s):  
Danilo T. M. P. Abreu ◽  
Marcos C. Maturana ◽  
Marcelo R. Martins ◽  
Siegberto R. Schenk

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.


2012 ◽  
Vol 616-618 ◽  
pp. 461-464
Author(s):  
Zhao Xia Liu ◽  
Lian Jun Chen

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.


PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254861
Author(s):  
Yaju Wu ◽  
Kaili Xu ◽  
Ruojun Wang ◽  
Xiaohu Xu

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.


Author(s):  
Ronald L. Boring ◽  
David I. Gertman ◽  
Jeffrey C. Joe ◽  
Julie L. Marble

A variety of methods have been developed to generate human error probabilities for use in the US nuclear power industry. When actual operations data are not available, it is necessary for an analyst to estimate these probabilities. Most approaches, including THERP, ASEP, SLIM-MAUD, and SPAR-H, feature an atomistic approach to characterizing and estimating error. The atomistic approach is based on the notion that events and their causes can be decomposed and individually quantified. In contrast, in the holistic approach, such as found in ATHEANA, the analysis centers on the entire event, which is typically quantified as an indivisible whole. The distinction between atomistic and holistic approaches is important in understanding the nature of human reliability analysis quantification and the utility and shortcomings associated with each approach.


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.


2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Evica Stojiljković ◽  
Bojan Bijelić ◽  
Marko Cvetković

Human reliability assessment is becoming increasingly important in risk assessment in all industrial systems. All methods for human reliability assessment are used to estimate human error probabilities, which is a measure of human reliability. Human error assessment is certainly a challenge fo r all the experts involved in risk assessment today. In Serbia, this issue has not received proper attention yet. Therefore, this paper presents the case study which confirmed that the usage of Absolute Probability Judgement for proper human reliability assessment. This approach was used in a case study of the Electric Power Company in Serbia (hereinafter EPCS) for the purpose of the analysis accident of a repair intervention on a 10/0.4 kV steel lattice tower “Nova Kolonija” (jurisdiction of EPCS, Veliki Trnovac, ED “Jugoistok”, Nis, Serbia). The case study performed at the EPCS has confirmed that the conventional APJ approach is not only highly applicable for quantification of human errors, but also comprehensive and simple to use in risk assessment of complex systems. Key words: Absolute Probability Judgement, Human Reliability Assessment, Accident, Case Study


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