Within maintenance activities and industrial operations, human is subjected to different kind of stresses and situation that could result in mistakes and accidents. The human errors in maintenance and manufacturing are an unexplored latter such that a little focusis invested in this area. The report aims to widen up the understanding of the human error in maintenance and manufacturing area. Aviation and marine operations are the most sectors that are subjected to human errors according tothe literature. There aredifferent types of human error that have effect on quality and overall effectivity. Human reliability models are one method to quantify human errors and usually used for the identification of human errors and HEP calculation. The most common reliability measurement methods are HEART, THERP and SLIM which are used depending on application and industry. As a part of efforts to define differences between those reliability models, literature including different industries is used and itis found that expert judgement influences the success and accuracy of such methods. There are many causes for human errors depending on the application but, communication and procedures followed are the most contributing factors. There is always a probability of existence of human errors as the mistake done by workers are inevitable. Industry 4.0 can help in decreasing human errors through the introduction of operator 4.0 as well as other approaches like training and upgrading organizational standards.

The presented doctoral thesis is focused on assessment of human factor impact on safety of industrial furnaces. Industrial furnaces are classified as machinery and belong to a group of industrial thermal equipment. The operation of industrial furnaces is burdened with the risks which the manufacturers and the furnace operators realize. The first part of the thesis presents an analysis of the current situation of legislation and scientific knowledges in the field of assessment of human factor impact on safety of industrial furnaces. In this part of the thesis the issue of human factor in other industrial branches: chemical industry, aviation and nuclear industry is also described. On the basis of conducted research the main aim of the thesis was established: preparation of a methodology for assessment of human factor impact on safety of industrial furnaces. Secondary targets were also listed. The second part deals with the assessment of risks connected with the operation of industrial furnaces and the assessment of human factor impact on safety of industrial furnaces. The furnace safety requirements, the process of furnaces risk assessment, the methods used for risk assessment and problems connected with the risk assessment are described. This part of the thesis explains the concept of human factor, presents a classification and description of the methods used for human reliability assessment, as well as the factors influencing the reliability of the operator. The third part of the thesis contains a proposed methodology for assessment of human factor impact on safety of industrial furnaces. Within the methodology performance shaping factors are stated, qualitative and quantitative assessment of human factor impact on safety of industrial furnaces is made and the system integration of the knowledges into the developed methodology is proposed – qualitative model for improvement of system state. The methodology is further verified on a real equipment – a hardening furnace.

The master thesis is focused on the reliability of the human factor in the operation of the production machine. In the first part the basic legislative requirements for safety and reliability of work are presented. In the second part, the reliability of the human factor is discussed along with a description of selected methods for assessing the reliability of the human factor. The research section then concludes with a proposed methodology for human factors assessment for the manufacturing process. In the practical part, the proposed methodology is applied to the selected process, which includes risk identification and outputs from the selected methods. The thesis then concludes by recommending preventive measures to eliminate the identified risks.

A l'ère actuelle de la décentralisation des ressources et des compétences, le recours aux technologies est devenu de plus en plus fréquent donnant naissance à la collaboration électronique désignée aussi par eCollaboration. Malgré tous les avantages qu'elle apporte, l'implication des technologies dans le processus de la collaboration est loin de résoudre tous ses problèmes. Les travaux de recherche tournant autour de l'évaluation de l'eCollaboration se focalisent sur la détection, l'explication et la résolution de ces problèmes. Dans la littérature, nous avons noté que les travaux d'évaluation accordent un intérêt particulier et non justifié à la composante technologique de l'eCollaboration aux dépens de sa composante humaine. Cette constatation a motivé notre travail de thèse qui a pour objectif de proposer une approche générique d'évaluation des scénarios d'eCollaboration qui prend en compte leurs composantes technologiques et humaines. Face à la délicatesse du contexte de l'eCollaboration et afin d'atteindre l'objectif fixé, notre contribution a suivi une démarche constituée de trois étapes. La première étape a consisté en une analyse basée sur les scénarios qui, suite à l'élaboration de plusieurs abstractions, nous a permis de proposer un modèle conceptuel de l'eCollaboration, un schéma des interactions mises en œuvre et une classification des scénarios d'eCollaboration. La deuxième étape s'est intéressée au développement d'un simulateur de scénarios d'eCollaboration en se basant sur les résultats produits par l'étape précédente. Ce travail nous a permis d'identifier un critère générique de succès de l'eCollaboration à travers la génération de différentes instances de scénarios d'eCollaboration se déroulant dans des conditions variées et l'observation de différents types d'anomalies qui y apparaissent. La troisième étape a porté sur la proposition d'une approche générique d'évaluation basée sur le critère de succès dégagé précédemment et composée de deux niveaux : le premier consiste en une détection des anomalies et le deuxième se focalise sur l'explication de celles-ci en se basant sur une méthode d'analyse de la fiabilité humaine qui considère que toute anomalie détectée est provoquée par une inadéquation des conditions de déroulement qui englobent des causes aussi bien technologiques que humaines. / In the actual area of resource and skills decentralization, the use of technologies has become increasingly common under the name of electronic collaboration or eCollaboration. In spite of its interest, the involvement of technologies in the collaboration process has not solved all its problems. The research works on eCollaboration evaluation are focused on the detection, explanation and resolution of these problems.In the literature, we noted that evaluation works accord a special and unjustified attention to technological component of eCollaboration scenarios at the expense of their human component. This finding has motivated our thesis, aiming to propose a generic evaluation approach of eCollaboration scenarios that considers technological and human eCollaboration components. Given the delicacy of eCollaboration environments and to attain the objective of this work, our contribution follows a three step based process. The first step consists in a scenario based analysis that allowed, after carrying out several abstractions, to suggest a conceptual eCollaboration model, a scheme of the implemented interactions and a classification of eCollaboration scenarios. The second step is focused on the development of an eCollaboration simulator based on results of the previous step. This work allowed us to identify a generic criterion for eCollaboration success by generating different instances of eCollaboration scenarios progressing in varied conditions and observing different types of anomalies occuring on them. The third step was interested in the proposition of a generic evaluation approach based on the success criterion previously detected and composed by two levels : the first is interested in anomaly detection and the second is focused on the explanation of it, using a human reliability analysis method which considers that any detected anomaly is due to inappropriate progress conditions including both technological and human causes.

L'homme reste l'un des éléments essentiels des opérations de transport modernes. Les méthodes d'analyse de la fiabilité humaine (HRA) fournissent une approche multidisciplinaire pour évaluer l'interaction entre les humains et le système. Cette thèse propose une nouvelle méthodologie HRA appelée PRELUDE (Performance shaping factor based human REliability assessment using vaLUation-baseD systems). Les facteurs de performance sont utilisés pour caractériser un contexte opérationnel dangereux. Le cadre de la théorie des fonctions de croyance et des systèmes d'évaluation (VBS) utilise des règles mathématiques pour formaliser l'utilisation de données d'experts et la construction d'un modèle de fiabilité humaine, il est capable de représenter toutes sortes d'incertitudes. Pour prédire la probabilité d'erreur humaine dans un contexte donné, et de fournir une remontée formelle pour réduire cette probabilité. La deuxième partie de ce travail démontre la faisabilité de PRELUDE avec des données empiriques. Un protocole pour obtenir des données à partir de simulateurs, et une méthode de transformation et d'analyse des données sont présentés. Une campagne expérimentale sur simulateur est menée pour illustrer la proposition. Ainsi, PRELUDE est en mesure d'intégrer des données provenant de sources (empiriques et expertes) et de types (objectifs et subjectifs) différents. Cette thèse aborde donc le problème de l'analyse des erreurs humaines, en tenant compte de l'évolution du domaine des méthodes HRA. Elle garde la facilité d'utilisation de l'industrie ferroviaire, fournissant des résultats qui peuvent facilement être intégrés avec les analyses de risques traditionnelles. Dans un monde de plus en plus complexe et exigeant, PRELUDE fournira aux opérateurs ferroviaires et aux autorités réglementaires une méthode permettant de s'assurer que le risque lié à l'interaction humaine est compris et géré de manière appropriée dans son contexte. / Humans are and remain one of the critical constituents of modern transport operations. Human Reliability Analysis (HRA) methods provide a multi-disciplinary approach: systems engineering and cognitive science methods to evaluate the interaction between humans and the system. This thesis proposes a novel HRA methodology acronymed PRELUDE (Performance shaping factor based human REliability assessment using vaLUation-baseD systEms). Performance shaping factors (PSFs) are used to characterize a dangerous operational context. The proposed framework of Valuation-based System (VBS) and belief functions theory (BFT) uses mathematical rules to formalize the use of expert data and construction of a human reliability model capable of representing all kinds of uncertainty. PRELUDE is able to predict the human error probability given a context, and also provide a formal feedback to reduce the said probability. The second part of this work demonstrates the feasibility of PRELUDE with empirical data from simulators. A protocol to obtain data, a transformation and data analysis method is presented. An experimental simulator campaign is carried out to illustrate the proposition. Thus, PRELUDE is able to integrate data from multiple sources (empirical and expert) and types (objective and subjective). This thesis, hence address the problem of human error analysis, taking into account the evolution of the HRA domain over the years by proposing a novel HRA methodology. It also keeps the rail industry’s usability in mind, providing a quantitative results which can easily be integrated with traditional risk analyses. In an increasingly complex and demanding world, PRELUDE will provide rail operators and regulatory authorities a method to ensure human interaction-related risk is understood and managed appropriately in its context.

Grades serve as one of the primary indicators of student learning, directing subsequent actions for students, instructors, and administrators, alike. Therefore, grade validity—that is, the extent to which grades communicate a meaningful and credible representation of what they purport to measure—is of utmost importance. However, a grade cannot be valid if one cannot trust that it will consistently and reliably result in the same value, regardless of who makes a measure or when they make it. Unfortunately, such reliability becomes increasingly challenging to achieve with larger class sizes, especially when utilizing multiple evaluators, as is often the case with mandatory introductory courses at large universities. Reliability suffers further when evaluating open-ended tasks, as are prevalent in authentic, high-quality engineering coursework.<div><br></div><div>This study explores grading reliability in the context of a large, multi-section engineering course. Recognizing the number of people involved and the plethora of activities that affect grading outcomes, the study adopts a systems approach to conduct a human reliability analysis using the Functional Resonance Analysis Method. Through this method, a collection of data sources, including course materials and observational interviews with undergraduate teaching assistant graders, are synthesized to produce a general model for how actions vary and affect subsequent actions within the system under study. Using a course assignment and student responses, the model shows how differences in contextual variables affect expected actions within the system. Next, the model is applied to each of the observational interviews with undergraduate teaching assistants to demonstrate how these actions occur in practice and to compare graders to one another and with expected behaviors. These results are further related to the agreement in system outcomes, or grades, assigned by each grader to guide analysis of how actions within the system affect its outcome.<br></div><div><br></div><div>The results of this study connect and elaborate upon previous models of grader cognition by analyzing the phenomenon in engineering, a previously unexplored context. The model presented can be easily generalized and adapted to smaller systems with fewer actors to understand sources of variability and potential threats to outcome reliability. The analysis of observed outcome instantiations guides a set of recommendations for minimizing grading variability.<br></div>

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Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T19:21:56Z No. of bitstreams: 1 D2014 - José Cristiano Pereira.pdf: 9830334 bytes, checksum: d5be51799514c74451d0ca3358d7757b (MD5) / Made available in DSpace on 2017-07-27T19:21:56Z (GMT). No. of bitstreams: 1 D2014 - José Cristiano Pereira.pdf: 9830334 bytes, checksum: d5be51799514c74451d0ca3358d7757b (MD5) / O processo de fabricação de motores a jato é complexo. Perigos e riscos e muitos elementos críticos estão presentes em milhares de atividades necessárias para fabricar um motor. Na investigação realizada nota-se a inexistência de um modelo específico para calcular quantitativamente a probabilidade de falha operacional de um motor à jato. O objetivo da tese foi desenvolver um modelo causal para análise de risco probabilística de falhas de motores a jato em situação operacional de fabricação. O modelo se caracteriza pela aplicação de rede Bayesiana associada à árvore de falha / árvore de evento e elicitação de probabilidades por especialistas para quantificar a probabilidade de falha. Para a concepção da construção do modelo, foi inicialmente desenvolvida uma pesquisa bibliométrica, através da consulta aos principais motores de busca nacionais e internacionais, em periódicos científicos e técnicos, bancos de dissertações/teses e eventos técnicos relacionados ao tema, para estabelecimento dos estado-da-arte e da técnica. Para a estimativa das probabilidades associadas aos cenários de falhas propostos, foi desenvolvido um processo de elicitação de probabilidade a partir da consulta a especialistas e técnicos. Na concepção do modelo foram consideradas três áreas de influência para a confiabilidade do sistema: humana, software e calibração. Como resultado foi desenvolvido o modelo CAPEMO, que é suportado por um aplicativo que utiliza a teoria das probabilidades (Lei de Bayes) para modelar incerteza. A probabilidade de falha estimada ao final da processo de fabricação, antes do motor ser colocado em operação, contribui no processo de tomada de decisão, melhoria da segurança do sistema e redução de riscos de falha do motor em operação / The process of jet engines manufacturing is complex. Hazards and risks and many critical elements are present in the thousands of activities required to manufacture an engine. In the conducted investigation it is observed a lack of a specific model to estimate quantitatively the probability of a jet engine operational failure. The goal of this thesis is to develop a causal model for probabilistic risk analysis of jet engines failure in manufacturing situational operation. The model is characterized by the application of Bayesian Network associated with the fault tree and event tree to quantify the probability of failure. For the establishment of state-of-the-art and technique and for the conception and construction of the model, a bibliometric research was conducted in the main national and international search engines, in the scientific and technical journals, in the database of dissertations/theses and technical events related to the topic. For the estimation of the probabilities associated with the proposed fault scenarios, a process of probability elicitation from technicians and experts was developed. In the design of the model three areas of influence for the reliability of the system were considered: human, software and calibration. As a result CAPEMO model was developed, that is supported by a software application that uses probability theory to model uncertainty. The probability of engine failure estimated at the end of the manufacturing process, before the motor be put into operation, helps in the allocation of resources in the decision-making process and improves system safety reducing the risk of engine failure in operation

[pt] A maioria dos estudos existentes em controle de qualidade de processos focam no desempenho de máquinas e ferramentas. Assim, estes já contam com bons métodos para serem controlados. Contudo, erros humanos em potencial estão presentes em todos os processos industriais que contenham a relação homem-máquina, fazendo com que a necessidade de se avaliar a qualidade do desempenho humano seja de igual importância. A abordagem para se avaliar quão suscetível à falha humana estão tais processos baseiam-se em probabilidades de erro, supondo que o desempenho humano funciona da mesma maneira que o desempenho de máquinas, ou em PSFs (Performance Shaping Factors), variáveis representativas de características de desempenho humano. Embora esta última abordagem seja mais eficiente, ainda existem críticas a sua falta de contextualização: tais características são avaliadas separadamente uma das outras, e independentemente da tarefa que o operador esteja realizando. Sistemas de Inferência Fuzzy (SIFs) permitem que variáveis lingüísticas sejam avaliadas em conjunto, isto é, passa a ser possível criar um modelo que assimile as nuances da variação do comportamento de um PSF concomitantemente com a alteração de outro PSF. Dessa forma, a caracterização da confiabilidade humana, considerando que diversos PSFs afetam no desempenho dos demais, pode ser satisfeita ao se fazer uso de SIFs interligados seqüencialmente - SIFs hierárquicos. Para se contextualizar a caracterização da confiabilidade humana por tarefa realizada, necessita-se que os PSFs pertinentes a cada determinada tarefa sejam medidos novamente e realimentados ao sistema (desenvolvido nesta dissertação). O SIF geral (composto por nove camadas de SIFs hierárquicos) foi testado com dados hipotéticos e dados reais de operadores e tarefas de uma empresa do setor elétrico brasileiro. Os resultados encontrados foram satisfatórios e evidenciaram que a Lógica Fuzzy, na forma de SIFs hierárquicos, pode ser utilizada para caracterizar a confiabilidade humana, com a vantagem de fazê-lo enquanto seu contexto é considerado. / [en] Most of existing studies in quality control focus on machinery performance. There are effective and advanced control methods to deal with that. However, potential human errors are present in every industrial process operated by humans. Therefore, evaluating the quality of human performance becomes as important as evaluate machinery s. The approach to evaluate how much processes are susceptible to human error are based on error probabilities, by assuming that human performance is similar to machinery performance, or on PSFs (Performance Shaping Factors) – variables representing human features. Although this based approach is more efficient, there are still criticisms about its lack of context awareness: those features are evaluated separately from one another, and regardless of which task the employee is performing. Fuzzy Inference Systems (FISs) allow linguistic variables to be evaluated simultaneously, thus making it possible to develop a method that gathers the nuances of behavioral changes of a PSF whilst another PSF varies. With this method, and considering that different PSFs affect the performance of others, human reliability can be assessed through the use of sequentially interconnected FISs – Hierarchical Fuzzy Inference Systems. In order to contextualize this assessment by tasks, each of the PSFs that affects each task will have to be measured and fed into the system (as developed within this dissertation) once per task and per employee. The main FIS (which contains nine layers of hierarchical FISs) was tested by using both hypothetical and real data from operators and tasks of a Brazilian electricity company. Results were satisfactory and attested that Fuzzy Logic, in the form of hierarchical FISs, can be used to assess human reliability, with the advantage of also taking the context into account.