Os programas de Ensaios de Prociência (EP) são utilizados pela sociedade para avaliar a competência e a confiabilidade de laboratórios na execução de medições específicas. Atualmente, diversos grupos de EP foram estabelecidos pelo INMETRO, entre estes, o grupo de testes de motores. Cada grupo é formado por diversos laboratórios que medem o mesmo artefato e suas medições são comparadas através de métodos estatísticos. O grupo de motores escolheu um motor gasolina 1.0, gentilmente cedido pela GM Powertrain, como artefato. A potência do artefato foi medida em 10 pontos de rotação por 6 laboratórios. Aqui, motivados por este conjunto de dados, estendemos o modelo de calibração comparativa de Barnett (1969) para avaliar a compatibilidade dos laboratórios considerando a distribuição t de Student e apresentamos os resultados obtidos das aplicações e simulações a este conjunto de dados / Proficiency Testing (PT) programs are used by society to assess the competence and the reliability in laboratories execution of specific measurements. Nowadays many PT groups were established by INMETRO, including the motor\'s test group. Each group is formed by laboratories measuring the same artifact and their measurements are compared through statistic methods. The motor\'s group chose a gasoline engine 1.0, kindly provided by GM as an artifact. The artifact\'s power was measured at ten points of rotation by 6 laboratories. Here, motivated by this set data, we extend the Barnet comparative calibration model (1969) to assess the compatibility of the laboratories considering the Student-t distribution and show the results obtained from application and simulation of this set data

Os programas de Ensaios de Prociência (EP) são utilizados pela sociedade para avaliar a competência e a confiabilidade de laboratórios na execução de medições específicas. Atualmente, diversos grupos de EP foram estabelecidos pelo INMETRO, entre estes, o grupo de testes de motores. Cada grupo é formado por diversos laboratórios que medem o mesmo artefato e suas medições são comparadas através de métodos estatísticos. O grupo de motores escolheu um motor gasolina 1.0, gentilmente cedido pela GM Powertrain, como artefato. A potência do artefato foi medida em 10 pontos de rotação por 6 laboratórios. Aqui, motivados por este conjunto de dados, estendemos o modelo de calibração comparativa de Barnett (1969) para avaliar a compatibilidade dos laboratórios considerando a distribuição t de Student e apresentamos os resultados obtidos das aplicações e simulações a este conjunto de dados / Proficiency Testing (PT) programs are used by society to assess the competence and the reliability in laboratories execution of specific measurements. Nowadays many PT groups were established by INMETRO, including the motor\'s test group. Each group is formed by laboratories measuring the same artifact and their measurements are compared through statistic methods. The motor\'s group chose a gasoline engine 1.0, kindly provided by GM as an artifact. The artifact\'s power was measured at ten points of rotation by 6 laboratories. Here, motivated by this set data, we extend the Barnet comparative calibration model (1969) to assess the compatibility of the laboratories considering the Student-t distribution and show the results obtained from application and simulation of this set data

Dans un contexte industriel compétitif, une prévision fiable du rendement est une information primordiale pour déterminer avec précision les coûts de production et donc assurer la rentabilité d'un projet. La quantification des risques en amont du démarrage d'un processus de fabrication permet des prises de décision efficaces. Durant la phase de conception d'un produit, les efforts de développement peuvent être alors identifiés et ordonnés par priorité. Afin de mesurer l'impact des fluctuations des procédés industriels sur les performances d'un produit donné, la construction de la probabilité du risque défaillance est développée dans cette thèse. La relation complexe entre le processus de fabrication et le produit conçu (non linéaire, caractéristiques multi-modales...) est assurée par une méthode de régression bayésienne. Un champ aléatoire représente ainsi, pour chaque configuration du produit, l'information disponible concernant la probabilité de défaillance. Après une présentation du modèle gaussien, nous décrivons un raisonnement bayésien évitant le choix a priori des paramètres de position et d'échelle. Dans notre modèle, le mélange gaussien a priori, conditionné par des données mesurées (ou calculées), conduit à un posterior caractérisé par une distribution de Student multivariée. La nature probabiliste du modèle est alors exploitée pour construire une probabilité de risque de défaillance, définie comme une variable aléatoire. Pour ce faire, notre approche consiste à considérer comme aléatoire toutes les données inconnues, inaccessibles ou fluctuantes. Afin de propager les incertitudes, une approche basée sur les ensembles flous fournit un cadre approprié pour la mise en œuvre d'un modèle bayésien imitant le raisonnement d'expert. L'idée sous-jacente est d'ajouter un minimum d'information a priori dans le modèle du risque de défaillance. Notre méthodologie a été mise en œuvre dans un logiciel nommé GoNoGo. La pertinence de cette approche est illustrée par des exemples théoriques ainsi que sur un exemple réel provenant de la société STMicroelectronics. / In competitive industries, a reliable yield forecasting is a prime factor to accurately determine the production costs and therefore ensure profitability. Indeed, quantifying the risks long before the effective manufacturing process enables fact-based decision-making. From the development stage, improvement efforts can be early identified and prioritized. In order to measure the impact of industrial process fluctuations on the product performances, the construction of a failure risk probability estimator is developed in this thesis. The complex relationship between the process technology and the product design (non linearities, multi-modal features...) is handled via random process regression. A random field encodes, for each product configuration, the available information regarding the risk of non-compliance. After a presentation of the Gaussian model approach, we describe a Bayesian reasoning avoiding a priori choices of location and scale parameters. The Gaussian mixture prior, conditioned by measured (or calculated) data, yields a posterior characterized by a multivariate Student distribution. The probabilistic nature of the model is then operated to derive a failure risk probability, defined as a random variable. To do this, our approach is to consider as random all unknown, inaccessible or fluctuating data. In order to propagate uncertainties, a fuzzy set approach provides an appropriate framework for the implementation of a Bayesian model mimicking expert elicitation. The underlying leitmotiv is to insert minimal a priori information in the failure risk model. Our reasoning has been implemented in a software called GoNoGo. The relevancy of this concept is illustrated with theoretical examples and on real-data example coming from the company STMicroelectronics.