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Accurate estimation of the coefficient of friction (CoF) is essential to accurately modeling railroad dynamics, reducing maintenance costs, and increasing safety factors in rail operations. The assumption of a constant CoF is popularly used in simulation studies for ease of implementation, however many evidences demonstrated that CoF depends on various dynamic parameters and instantaneous conditions. In the real world, accurately estimating the CoF is difficult due to effects of various uncertain parameters, such as wheel and rail materials, rail roughness, contact patch, and so on. In this study, the newly developed 3-D nonlinear CoF model for the dry rail condition is introduced and the CoF variation is tested using this model with dynamic parameters estimated from the wheel-rail simulation model. In order to account for uncertain parameters, a stochastic analysis using the polynomial chaos (poly-chaos) theory is performed using the CoF and wheel-rail dynamics models. The wheel-rail system at a right traction wheel is modeled as a mass-spring-damper system to simulate the basic wheel-rail dynamics and the CoF variation. The wheel-rail model accounts for wheel-rail contact, creepage effect, and creep force, among others. Simulations are performed at train speed of 20 m/s for 4 sec using rail roughness as a unique excitation source. The dynamic simulation has been performed for the deterministic model and for the stochastic model. The dynamics results of the deterministic model provide the starting point for the uncertainty analysis. Six uncertain parameters have been studied with an assumption of 50% uncertainty, intentionally imposed for testing extreme conditions. These parameters are: the maximum amplitude of rail roughness (MARR), the wheel lateral displacement, the track stiffness and damping coefficient, the sleeper distance, and semi-elliptical contact lengths. A symmetric beta distribution is assumed for these six uncertain parameters. The PDF of the CoF has been obtained for each uncertain parameter study, for combinations of two different uncertain parameters, and also for combinations of three different uncertain parameters. The results from the deterministic model show acceptable vibration results for the body, the wheel, and the rail. The introduced CoF model demonstrates the nonlinear variation of the total CoF, the stick component, and the slip component. In addition, it captures the maximum CoF value (initial peak) successfully. The stochastic analysis results show that the total CoF PDF before 1 sec is dominantly affected by the stick phenomenon, while the slip dominantly influences the total CoF PDF after 1 sec. Although a symmetric distribution has been used for the uncertain parameters considered, the uncertainty in the response obtained displayed a skewed distribution for some of the situations investigated. The CoF PDFs obtained from simulations with combinations of two and three uncertain parameters have wider PDF ranges than those obtained for only one uncertain parameter. FFT analysis using the rail displacement has been performed for the qualitative validation of the stochastic simulation result due to the absence of the experimental data. The FFT analysis of the deterministic rail displacement and of the stochastic rail displacement with uncertainties demonstrates consistent trends commensurate with loss of tractive efficiency, such as the bandwidth broadening, peak frequency shifts, and side band occurrence. Thus, the FFT analysis validates qualitatively that the stochastic modeling with various uncertainties is well executed and is reflecting observable, real-world results. In conclusions, the development of an effective model which helps to understand the nonlinear nature of wheel-rail friction is critical to the progress of railroad component technology and rail safety. In the real world, accurate estimation of the CoF at the wheel-rail interface is very difficult since it is influenced by several uncertain parameters as illustrated in this study. Using the deterministic CoF value can cause underestimation or overestimation of CoF values leading to inaccurate decisions in the design of the wheel-rail system. Thus, the possible PDF ranges of the CoF according to key uncertain parameters must be considered in the design of the wheel-rail system. / Ph. D.

Yes / We provide multi-factor real option models (and quasi-analytical solutions) for equipment capital budgeting under uncertainty, when there is either unexpected, or anticipated, or uncertain (volatile) technological progress. We calculate the threshold level of revenues and operating costs using the incumbent equipment that would justify replacement. Replacement is deferred for lower revenue thresholds. If progress is anticipated or highly uncertain, alert financial managers should wait longer before replacing equipment. Replacement deferral increases with decreases in the expected correlation between revenue and operating costs, and with increases in the revenue and/or operating cost volatility. Uncertain technological progress increases the real option value of waiting. The best approach for equipment suppliers is to reduce the expected revenue and/or cost volatility, and/or reduce the expected uncertainty of technological innovations, since then an incentive exists for the early replacement of old equipment when a technologically advanced version is launched.

[pt] Os sistemas de inferência fuzzy são técnicas de aprendizado de máquina que possuem a capacidade de modelar incertezas matematicamente. Eles são divididos em sistemas de inferências fuzzy tipo-1 e fuzzy tipo-2. O sistema de inferência fuzzy tipo-1 vem sendo amplamente aplicado na solução de diversos problemas referentes ao aprendizado de máquina, tais como, controle, classificação, clusterização, previsão, dentre outros. No entanto, por apresentar uma melhor modelagem matemática das incertezas, o sistema de inferência fuzzy tipo-2 vem ganhando destaque ao longo dos anos. Esta melhora modelagem vem também acompanhada de um aumento do esforço matemático e computacional. Visando reduzir tais pontos para solucionar problemas de classificação, este trabalho apresenta o desenvolvimento e a comparação de duas funções de pertinência Gaussiana para um sistema de inferência fuzzy tipo-2 intervalar usando o método superior e inferior. São utilizadas as funções de pertinência Gaussiana com incerteza na média e com incerteza no desvio padrão. Ambos os modelos fuzzy abordados neste trabalho são treinados por algoritmos baseados em informações de primeira ordem. Além disso, este trabalho propõe a extensão dos modelos fuzzy tipo-2 intervalar para apresentarem múltiplas saídas, reduzindo significativamente o custo computacional na solução de problemas de classificação multiclasse. Finalmente, visando contextualizar a utilização desses modelos em aplicações de engenharia mecânica, este trabalho apresenta a solução de um problema de detecção de falhas em turbinas a gás, utilizadas em aeronaves. / [en] Fuzzy logic systems are machine learning techniques that can model mathematically uncertainties. They are divided into type-1 fuzzy, and type-2 fuzzy logic systems. The type-1 fuzzy logic system has been widely applied to solve several problems related to machine learning, such as control, classification, clustering, prediction, among others. However, as it presents a better mathematical modeling of uncertainties, the type-2 fuzzy logic system has received much attention over the years. This modeling improvement is also accompanied by an increase in mathematical and computational effort. Aiming to reduce these issues to solve classification problems, this work presents the development and comparison of two Gaussian membership functions for a type-2 interval fuzzy logic system using the upper and lower method. Gaussian membership functions with uncertainty in the mean and with uncertainty in the standard deviation are used. Both fuzzy models covered in this work are trained by algorithms based on first order information. Furthermore, this work proposes the extension of interval type-2 fuzzy models to present multiple outputs, significantly reducing the computational cost in solving multiclass classification problems. Finally, aiming to contextualize the use of these models in mechanical engineering applications, this work presents the solution of a problem of fault detection in aircraft gas turbines.

Real-life transportation systems are subject to numerous uncertainties in their operation. Researchers have suggested various reliability measures to characterize their network-level performances. One of these measures is given by travel time reliability, defined as the probability that travel times remain below certain (acceptable) levels. Existing reliability assessment (and optimization) techniques tend to be computationally intensive. In this dissertation we develop computationally efficient alternatives. In particular, we make the following three contributions. In the first contribution, we present a novel reliability assessment methodology when the source of uncertainty is given by road capacities. More specifically, we present a method based on the theory of Fourier transforms to numerically approximate the probability density function of the (system-wide) travel time. The proposed methodology takes advantage of the established computational efficiency of the fast Fourier transform. In the second contribution, we relax the common assumption that probability distributions of the sources of uncertainties are known explicitly. In reality, this distribution may be unavailable (or inaccurate) as we may have no (or insufficient) data to calibrate the distributions. We present a new method to assess travel time reliability that is distribution-free in the sense that the methodology only requires that the first N moments (where N is any positive integer) of the travel time to be known and that the travel times reside in a set of known and bounded intervals. Instead of deriving exact probabilities on travel times exceeding certain thresholds via computationally intensive methods, we develop analytical probability inequalities to quickly obtain upper bounds on the desired probability. Because of the computationally intensive nature of (virtually all) existing reliability assessment techniques, the optimization of the reliability of transportation systems has generally been computationally prohibitive. The third and final contribution of this dissertation is the introduction of a new transportation network design model in which the objective is to minimize the unreliability of travel time. The computational requirements are shown to be much lower due to the assessment techniques developed in this dissertation. Moreover, numerical results suggest that it has the potential to form a computationally efficient proxy for current simulation-based network design models. / text

Pour synthétiser un correcteur robuste pour un système linéaire incertain, il existe de nombreuses méthodes linéaires. Cependant, bien souvent, le gain en robustesse se fait au détriment de la performance. Aussi, dans cette thèse, on s'intéresse à la situation où la plage des valeurs possibles des paramètres est "très grande" par rapport à la "faible" variation du niveau de performance souhaité. Dans cette situation, il peut alors s'avérer intéressant d'utiliser des correcteurs séquencés. Seulement, la mise en place de cette solution nécessite que le correcteur ait à sa disposition les paramètres sur lesquels il sera séquencé. Et il peut arriver que l'on ne souhaite pas (à cause de considérations de réalisation pratique), ou que l'on ne puisse pas disposer de la mesure de ces paramètres. On est alors amené à estimer ces paramètres et donc à utiliser le paradigme de la commande adaptative. Dans cette thèse, on cherche à proposer une méthodologie de synthèse d'un correcteur auto-adaptatif afin de résoudre un problème de commande robuste d'un procédé linéaire incertain. Après une étude théorique ayant pour objectif de proposer une telle méthodologie, le cas d'un avion instable est traité à titre d'application, permettant ainsi de mettre en évidence le bénéfice que la stratégie proposée peut apporter à la commande d'un système incertain. / Many linear methods exist to design a robust controller for an uncertain linear system. This thesis considered the situation where the range of possible values of parameters is "very large" in relation to "small" variations in the desired level of performance. Frequently, an increase in robustness is obtained at the expense of a performance loss. The use of scheduled controllers may be an innovative way to address this problem. The implementation of this solution requires the controller has at its disposal the parameters on which the scheduling is done. However, it may occur that making the measure of the parameters available is not desired (for example, because of practical implementation aspects) or not possible. In these situations, the designer of the controller is led to estimate these parameters and then to use the paradigm of adaptive control. This thesis explored a methodology for designing an adaptive controller in which to solve the problem of robust control for an uncertain linear plant. A theoretical study was first undertaken which aimed to propose such a methodology; followed by, a study of the case of an unstable airplane as an application. Such an analysis highlighted the benefits that the proposed strategy can bring to the control for an uncertain plant.

Dans le cadre de la présente thèse, on a procédé à l’étude du comportement dynamique d’un système d’engrenage comportant des paramètres incertains. Une des principales hypothèses faite dans l’utilisation des méthodes de prise en compte des incertitudes, est que le modèle est déterministe, c’est-à-dire que les paramètres utilisés dans le modèle ont une valeur définie et invariante. Par ailleurs, la connaissance du domaine de variation de la réponse dynamique du système dues aux incertitudes qui découle des coefficients d’amortissement, des raideurs d’engrènement, la présence de frottement entre les pièces, les défauts de montage et de fabrication ou l’inertie des pales dans le cas d’éolienne est essentielle. Pour cela, dans la première partie, on s’applique à décrire la réponse dynamique d’une transmission par engrenage comportant des paramètres modélisés par des variables aléatoires. Pour ce faire, nous utilisons la simulation de Monte Carlo, la méthode de perturbation et la méthode de projection sur un chaos polynomial. Dans la seconde partie,deux approches sont utilisées pour analyser le comportement dynamique d’un système d’engrenage d’éolienne : l’approche probabiliste et l’approche ensembliste basée sur la méthode d’analyse par intervalles. L'objectif consiste à comparer les deux approches pour connaitre leurs avantages et inconvénients en termes de précision et temps de calcul. / In the present work, the dynamic behavior of a gear system with uncertain parameters is studied. One of the principal hypotheses in the use of methods for taking into account uncertainties is that the model is deterministic, that is to say that parameters used in the model have a defined and fixed value. Furthermore, the knowledge of variation response of a gear system involving damping coefficients, mesh stiffness, friction coefficient, assembly defect, manufacturing defect or the input blades in the case of wind turbine is essential. In the first part, we investigate the dynamic response of a gear system with uncertain parameters modeled as random variables. A Monte Carlo simulation, a perturbation method and a polynomial chaos method are carried out. In the second part, two approaches are used to analyze the dynamic behavior of a wind turbine gear system : the probabilistic approach and the interval analysis method. The objective is to compare the two approaches to define their advantages and disadvantages in terms of precision and computation time.

Avec l'émergence du mouvement Open Data, des centaines de milliers de sources de données provenant de divers domaines (e.g., santé, gouvernementale, statistique, etc.) sont maintenant disponibles sur Internet. Ces sources de données sont accessibles et interrogées via des services cloud DaaS, et cela afin de bénéficier de la flexibilité, l'interopérabilité et la scalabilité que les paradigmes SOA et Cloud Computing peuvent apporter à l'intégration des données. Dans ce contexte, les requêtes sont résolues par la composition de plusieurs services DaaS. Définir la sémantique des services cloud DaaS est la première étape vers l'automatisation de leur composition. Une approche intéressante pour définir la sémantique des services DaaS est de les décrire comme étant des vues sémantiques à travers une ontologie de domaine. Cependant, la définition de ces vues sémantiques ne peut pas être toujours faite avec certitude, surtout lorsque les données retournées par un service sont trop complexes. Dans cette thèse, nous proposons une approche probabiliste pour représenter les services DaaS à sémantique incertaine. Dans notre approche, un service DaaS dont la sémantique est incertaine est décrit par plusieurs vues sémantiques possibles, chacune avec une probabilité. Les services ainsi que leurs vues sémantiques possibles sont représentées dans un registre de services probabiliste (PSR). Selon les dépendances qui existent entre les services, les corrélations dans PSR peuvent être représentées par deux modèles différents : le modèle Bloc-indépendant-disjoint (BID), et le modèle à base des réseaux bayésiens. En se basant sur nos modèles probabilistes, nous étudions le problème de l'interprétation d'une composition existante impliquant des services à sémantique incertaine. Nous étudions aussi le problème de la réécriture de requêtes à travers les services DaaS incertains, et nous proposons des algorithmes efficaces permettant de calculer les différentes compositions possibles ainsi que leurs probabilités. Nous menons une série d'expérimentation pour évaluer la performance de nos différents algorithmes de composition. Les résultats obtenus montrent l'efficacité et la scalabilité de nos solutions proposées / With the emergence of the Open Data movement, hundreds of thousands of datasets from various concerns (e.g., healthcare, governmental, statistic, etc.) are now freely available on Internet. A good portion of these datasets are accessed and queried through Cloud DaaS services to benefit from the flexibility, the interoperability and the scalability that the SOA and Cloud Computing paradigms bring to data integration. In this context, user’s queries often require the composition of multiple Cloud DaaS services to be answered. Defining the semantics of DaaS services is the first step towards automating their composition. An interesting approach to define the semantics of DaaS services is by describing them as semantic views over a domain ontology. However, defining such semantic views cannot always be done with certainty, especially when the service’s returned data are too complex. In this dissertation, we propose a probabilistic approach to model the semantic uncertainty of data services. In our approach, a DaaS service with an uncertain semantics is described by several possible semantic views, each one is associated with a probability. Services along with their possible semantic views are represented in probabilistic service registry (PSR).According to the services dependencies, the correlations in PSR can be represented by two different models :Block-Independent-Disjoint model (noted BID), and directed probabilistic graphical model (Bayesian network). Based on our modeling, we study the problem of interpreting an existing composition involving services with uncertain semantics. We also study the problem of compositing uncertain DaaS services to answer a user query, and propose efficient methods to compute the different possible compositions and their probabilities. We conduct a series of experiments to evaluate the performance of our composition algorithms. The obtained results show the efficiency and the scalability of our proposed solutions