The condition monitoring of multi-component systems utilizes multiple sensors to capture the functional condition of the systems and allows the sensor information to be used to reason about the health information of the systems or components. Chapter 3 considers the situation when sensor signals capture unknown mixtures of component signals and proposes a two-stage vibration-based methodology to identify component degradation signals from mixed sensor signals in order to predict component-level residual lives. Specifically, we are interested in modeling the degradation of systems that consist of two or more identical components operating under similar conditions. Chapter 4 focuses on the interactive relationship between tool wear (component degradation) and product quality degradation (sensor information) that widely exists in multistage manufacturing processes and proposes a high-dimensional stochastic differential equation model to capture the interaction relationship. Then, real-time quality measurements are incorporated to online predict the residual life of the system. Chapter 5 develops a strategy of dynamic workload adjustment for parallel multi-component systems in order to control the degradation processes and failure times of individual components, for the purpose of preventing the overlap of component failures. This chapter opens a new research direction that focuses on the active control of degradation rather than only the modeling part.

No description available.

This dissertation will investigate how Product Performance Contracts are organized in Multi Component Systems contexts that proliferate contemporary OEM industries. The last two decades have seen a big change in both practice as well as the product engineering technologies that form the ecosystem within which suppliers and buyers negotiate the scale and scope of their transaction contracts. While we have seen the focus of industrial procurement move from specifications based contracts to performance based contracts, we are also witnessing a burgeoning technological capability that allows remote monitoring of product performance. These capabilities are part of the interconnectivity driving the much-touted Internet of Things (IoT) technology and at the heart of the Industrial Big Data ecosystem. The dissertation will attempt to explain three major phenomena in the industrial buyer and seller relationship in the context of Multi Component System Industries. First, we uncover the factors that explain the choice of product performance contract specificity between the OEM and suppliers. We first set up an analytical model to explain the notion of an optimal contract specificity level and predict and further empirically test the role of different factors in the choice of contract specificity. We find that while the technology uncertainty decreases the level of optimal contract specificity, OEM’s transaction specific investment, unconstrained mixing-and-matching of branded component, and extent of product monitoring technology increases the level of optimal contract specificity. Second, we provide empirical evidence that any deviation from optimal contract specificity erodes value in the form of an increase in total transaction cost. In our transaction cost efficiency model, we also illustrate with a precise granularity that under-specified contracts lead to more ex-post dispute costs, and over-specified contracts lead to more ex-post contract monitoring cost and ex-ante contract writing cost. Third, we investigate how contracts, investments in strategic capabilities such as monitoring technology, the overall firm strategy, and transaction costs determine the firm performance. We find that not every transaction cost is a dead weight loss in terms of product performance. Most notably we find that ex-post dispute costs are associated with higher product performance when there is a major incident such as component failure between the OEM and the supplier. Methodologically, this dissertation proposes to use a combination of field work, mathematical modeling, conceptual theory building, and empirical analysis of primary data about firm practices. / Thesis / Doctor of Philosophy (PhD)

Nowadays, earthquakes are one of the most catastrophic natural events that have a significant human, socio-economic and environmental impact. Besides, based on both observations of damage following recent major/moderate seismic events and numerical/experimental studies, it clearly emerges that critical non-structural components (NSCs) that are ubiquitous to most industrial facilities are particularly and even disproportionately vulnerable to those events. Nonetheless and despite their great importance, seismic provisions for industrial facilities and their process equipment are still based on the classical load-and-resistance factor design (LRFD) approach; a performance-based earthquake engineering (PBEE) approach should, instead, be preferred. Along this vein, in recent years, much research has been devoted to setting computational fragility frameworks for special-risk industrial components and structures. However, within a PBEE perspective, studies have clearly remarked: i) a lack of definition of performance objectives for NSCs; ii) the need for fully comprehensive testing campaigns data on coupling effects between main structures and NSCs. In this respect, this doctorate thesis introduces a computational framework for an efficient and accurate seismic state-dependent fragility analysis; it is based on a combination of data acquired from an extensive experimental shake table test campaign on a full-scale prototype industrial steel frame structure and the most recent surrogate-based UQ forward analysis advancements. Specifically, the framework is applied to a real-world application consisting of seismic shake table tests of a representative industrial multi-storey frame structure equipped with complex process components, carried out at the EUCENTRE facility in Italy, within the European SPIF project: Seismic Performance of Multi-Component Systems in Special Risk Industrial Facilities. The results of this experimental research campaign also aspire to improve the understanding of these complex systems and improve the knowledge of FE modelling techniques. The main goals aim to reduce the huge computational burden and to assess, as well, when the importance of coupling effects between NSCs and the main structure comes into play. Insights provided by innovative monitoring systems were then deployed to develop and validate numerical and analytical models. At the same time, the adoption of Der Kiureghian's stochastic site-based ground motion model (GMM) was deemed necessary to severely excite the process equipment and supplement the scarcity of real records with a specific frequency content capable of enhancing coupling effects. Finally, to assess the seismic risk of NSCs of those special facilities, this thesis introduces state-dependent fragility curves that consider the accumulation of damage effects due to sequential seismic events. To this end, the computational burden was alleviated by adopting polynomial chaos expansion (PCE) surrogate models. More precisely, the dimensionality of a seismic input random vector has been reduced by performing the principal component analysis (PCA) on the experimental realizations. Successively, by bootstrapping on the experimental design, separate PCE coefficients have been determined, yielding a full response sample at each point. Eventually, empirical state-dependent fragility curves were derived.

Made available in DSpace on 2014-12-17T14:08:38Z (GMT). No. of bitstreams: 1 Ernesto Vargas Bautista_DISSERT_01_59.pdf: 4396872 bytes, checksum: 70a69eb1e9973135d541fd7ce76f4ed9 (MD5) Previous issue date: 2010-02-26 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / In Brazil and around the world, oil companies are looking for, and expected development of new technologies and processes that can increase the oil recovery factor in mature reservoirs, in a simple and inexpensive way. So, the latest research has developed a new process called Gas Assisted Gravity Drainage (GAGD) which was classified as a gas injection IOR. The process, which is undergoing pilot testing in the field, is being extensively studied through physical scale models and core-floods laboratory, due to high oil recoveries in relation to other gas injection IOR. This process consists of injecting gas at the top of a reservoir through horizontal or vertical injector wells and displacing the oil, taking advantage of natural gravity segregation of fluids, to a horizontal producer well placed at the bottom of the reservoir. To study this process it was modeled a homogeneous reservoir and a model of multi-component fluid with characteristics similar to light oil Brazilian fields through a compositional simulator, to optimize the operational parameters. The model of the process was simulated in GEM (CMG, 2009.10). The operational parameters studied were the gas injection rate, the type of gas injection, the location of the injector and production well. We also studied the presence of water drive in the process. The results showed that the maximum vertical spacing between the two wells, caused the maximum recovery of oil in GAGD. Also, it was found that the largest flow injection, it obtained the largest recovery factors. This parameter controls the speed of the front of the gas injected and determined if the gravitational force dominates or not the process in the recovery of oil. Natural gas had better performance than CO2 and that the presence of aquifer in the reservoir was less influential in the process. In economic analysis found that by injecting natural gas is obtained more economically beneficial than CO2 / No Brasil e no mundo, as empresas de petr?leo est?o ? procura, desenvolvimento e ? espera de novas tecnologias e processos que possam elevar o fator de recupera??o de ?leo em seus reservat?rios, com um equilibrado custo-benef?cio. Na procura de novas tecnologias foi desenvolvido o processo de drenagem gravitacional assistida por g?s (GAGD Gas Assisted Gravity Drainage) que foi classificado dentro dos m?todos de recupera??o avan?ada de ?leo. O processo GAGD, que se encontra em fase de teste piloto em campo, est? sendo muito estudado atrav?s de modelos f?sicos em escala de laborat?rio e em testemunhos, isto devido ?s altas recupera??es de ?leo em rela??o aos outros processos de inje??o de g?s. Este m?todo consiste em injetar g?s no topo de um reservat?rio atrav?s de po?os injetores verticais ou horizontais e deslocar o ?leo, aproveitando a segrega??o gravitacional natural dos fluidos, para um po?o produtor horizontal colocado na base do reservat?rio. Para estudar este processo foi idealizado um reservat?rio homog?neo e um modelo de fluido multicomponente com caracter?sticas parecidas aos campos de ?leo leve do Brasil para que, atrav?s de um simulador composicional, se analisem os par?metros operacionais. O processo foi simulado no programa comercial GEM da CMG (Computer Modelling Group). Os par?metros operacionais estudados foram a vaz?o de inje??o de g?s, o tipo de g?s injetado (g?s natural e CO2), a localiza??o do po?o injetor e do po?o produtor. Tamb?m foi estudada a presen?a de aq??fero no processo. Os resultados mostraram que o m?ximo espa?amento vertical entre os dois po?os, provocou a m?xima recupera??o de ?leo no GAGD. Tamb?m, verificou-se que a maiores vaz?es de inje??o, se obtiveram os maiores fatores de recupera??o. Este par?metro controlou a velocidade da frente do g?s injetado e determinou se a for?a gravitacional domina ou n?o sobre o processo na recupera??o de ?leo. O g?s natural teve melhor desempenho que o CO2 e que, a presen?a de aq??fero no reservat?rio influenciou pouco no processo. Na an?lise econ?mica verificou-se que a inje??o de g?s natural obteve o melhor beneficio econ?mico que com CO2

Face au défi des entreprises pour le maintien de leurs équipements au maximum de leur fiabilité, de leur disponibilité, de leur rentabilité et de leur sécurité au coût de maintenance minimum, des stratégies de maintenance telles que le CBM et le PHM ont été développées. Pour mettre en œuvre ces stratégies, comme pour la planification des activités de production il est nécessaire de connaître l’aptitude des systèmes à réaliser les futures tâches afin de réaliser le séquencement des opérations de production et de maintenance. Cette thèse présente les éléments d'une fonction générique qui évalue la capacité des systèmes techniques multi-composants à exécuter les tâches de production de biens ou de services assignées. Ce manuscrit présente une proposition de modélisation de systèmes techniques multi-composants représentant les différentes entités qui les composent, leurs états et leurs relations. Plusieurs types d’entités ont été identifiés. Pour chacun d’eux, des inférences sont proposées pour définir à l’intérieur du système l’aptitude de l’entité à accomplir les futures tâches de production à partir des évaluations de son état présent et futur et des évaluations des états présents et futurs des autres entités avec lesquelles elle est en relation. Ces évaluations des états présents et futurs sont basées sur l’exploitation de pronostics locaux des composants. Ces pronostics sont des prévisions qui intrinsèquement comportent des incertitudes pouvant être aléatoires ou épistémiques. La fonction proposée et les inférences prennent en compte ces deux formes d’incertitudes. Pour cela, les traitements et la fonction proposée exploite des éléments de la théorie de Dempster-Shafer. La modélisation des systèmes multi-composants pouvant être représentée sous la forme de graphes dont les états des nœuds dépendent de données comportant des incertitudes, des éléments des réseaux bayésiens sont également utilisés. Cette fonction fournit des indicateurs, sur l’aptitude de chaque entité du système à accomplir les futures tâches de production mais aussi indique les composants nécessitant une intervention afin d’améliorer cette aptitude. Ainsi, ces indicateurs constituent les éléments d'aide à la décision pour la planification des opérations de maintenance de façon conditionnelle et préventive, mais aussi pour la planification des opérations de production. / One major challenge of companies consists in maintaining their technical production resources at the maximum level of reliability, availability, profitability and safety for a minimum maintenance cost, maintenance strategies such as CBM and PHM have been developed. To implement these strategies, as with production activity planning, it is necessary to know the ability of systems to perform future tasks to order production and maintenance operations. This thesis presents the generic function that evaluates the ability of multi-component technical systems to perform the production tasks of goods or services. This manuscript presents a proposal for the modelling of multi-component technical systems representing the different entities that compose them, their states and their relationships. Several types of entities have been identified. For each of them, inferences are proposed to define within the system the entity's ability to perform future production tasks based on its own assessment of its present and future state and the assessments of the present and future states of the other entities with which it is involved. These assessments of present and future states are based on the use of local prognoses of components. These prognoses are predictions that inherently involve uncertainties that may be aleatory or epistemic. The proposed function and inferences take into account these two kinds of uncertainty. To do this, the inferences and the proposed function uses elements of the Dempster-Shafer theory. Since the modelling of multi-component systems can be represented in the form of graphs whose node states depend on data with uncertainties, elements of Bayesian networks are also used. This function provides indicators on the ability of each system entity to perform future production tasks but also indicates the components that require maintenance to improve this ability. Thus, these indicators constitute the elements of decision support for the planning of maintenance operations in a conditional and preventive way, but also for the planning of production tasks.

We study physical systems composed of at least two immiscible fluids occu- pying different regions of space, the so-called phases. Flows of such multi-phase fluids are frequently met in industrial applications which rises the need for their numerical simulations. In particular, the research conducted herein is motivated by the need to model the float glass forming process. The systems of interest are in the present contribution mathematically described in the framework of the so-called diffuse interface models. The thesis consists of two parts. In the modelling part, we first derive standard diffuse interface models and their generalized variants based on the concept of multi-component continuous medium and its careful thermodynamic analysis. We provide a critical assessment of assumptions that lead to different models for a given system. Our newly formulated class of generalized models of Cahn-Hilliard-Navier-Stokes-Fourier (CHNSF) type is applicable in a non-isothermal setting. Each model belonging to that class describes a mixture of separable, heat conducting Newtonian fluids that are either compressible or incompressible. The models capture capillary and thermal effects in thin interfacial regions where the fluids actually mix. In the computational part, we focus on the development of an efficient and robust...