Model-Based High-Dimensional Pose Estimation with Application to Hand Tracking

This thesis presents novel techniques for computer vision based full-DOF human hand motion estimation. Our main contributions are: A robust skin color estimation approach; A novel resolution-independent and memory efficient representation of hand pose silhouettes, which allows us to compute area-based similarity measures in near-constant time; A set of new segmentation-based similarity measures; A new class of similarity measures that work for nearly arbitrary input modalities; A novel edge-based similarity measure that avoids any problematic thresholding or discretizations and can be computed very efficiently in Fourier space; A template hierarchy to minimize the number of similarity computations needed for finding the most likely hand pose observed; And finally, a novel image space search method, which we naturally combine with our hierarchy. Consequently, matching can efficiently be formulated as a simultaneous template tree traversal and function maximization

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics

Machine learning and simulation for the optimisation and characterisation of electrodes in batterie

The performance of electrochemical energy storage (EES) and energy conversion (EC) technologies is closely related to their electrode microstrcuture. Thus, this work focuses on the development of two novel computational models for the characterisation and optimisation of electrodes for three devices: Redox Flow batteries (RFBs), Solid Oxide Fuel Cells (SOFCs), and Lithium-ion batteries (LIBs). The first method introduces a Pore Network Model (PNM) for simulating the coupled charge and mass transport processes within electrodes. This approach is implemented for a vanadium RFB using different commercially available carbon-based electrodes. The results from the PNM show non-uniformity in the concentration and current density distributions within the electrode, which leads to a fast discharge due to regions where mass-transport limitations are predominant. The second approach is based on the stochastic reconstruction of synthetic electrode microstructures. For this purpose, a deep convolutional generative adversarial network (DC-GAN) is implemented for generating three-dimensional n-phase microstructures of a LIB cathode and a SOFC anode. The results show that the generated data is able to represent the morphological properties and two-point correlation function of the real dataset. As a subsequent process, a generation-optimisation closed-loop algorithm is developed using Gaussian Process Regression and Bayesian optimisation for the design of microstructures with customised properties. The results show the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as an optimisation of these properties constrained by constant values of volume fraction. Overall, this work presents a comprehensive analysis of the effect of the electrode microstructure in the performance of different energy storage devices. The introduction of a PNM bridges the gap between volume-averaged continuum models and detailed the pore-scale models. The main advantage of this model is the ability to visually show the concentration and current distributions inside the electrode within a reasonably low computational time. Based on this, this work represents the first visual showcase of how regions limited by low convective flow affect the rate of discharge in an electrode, which is essential for the design of optimum electrode microstructures. The implementation of DC-GANs allows for the first time the fast generation of arbitrarily large synthetic microstructural volumes of n-phases with realistic properties and with periodic boundaries. The fact that the generator constitutes a virtual representation of the real microstructure allows the inclusion of the generator as a function of the input latent space in a closed-loop optimisation process. For the first time, a set of visually realistic microstructures of a LIB cathode with user-specified morphological properties were designed based on the optimisation of the generator’s latent space. The introduction of a closed-loop generation-optimisation approach represents a breakthrough in the design of optimised electrodes since it constitutes a first approach for evaluating the microstructure-performance correlation in a continuous forward and backward process.Open Acces

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Multidisciplinary Design Optimization of Electric Aircraft Considering Systems Modeling and Packaging

Electric aircraft propulsion is an intriguing path towards sustainable aviation, but the technological challenges are significant. Bulky and heavy electrical components such as batteries create spatial integration and aircraft performance challenges, especially for longer-range aircraft. A common thread among all aircraft with electric propulsion is the close coupling of aircraft design disciplines, such as aerodynamics, structures, propulsion, controls, and thermal management. Multidisciplinary design optimization (MDO) is a promising technique for solving design problems with many closely-coupled physical disciplines. The first half of this dissertation focuses on MDO of electric aircraft considering systems modeling. First, design of electric aircraft is reviewed in detail from the perspective of the various disciplines. Next, methods and models for electric aircraft propulsion systems are introduced. A case study involving a general aviation airplane is explored in order to validate the performance of the methods and generate some insight into the tradespace for series hybrid aircraft. The systems modeling approach is then extended to include basic thermal management systems. The prior case study is revisisted while considering thermal constraints. Impact of thermal management on aircraft performance is assessed. The thermal management analysis methods are validated using flight test data from the Pipistrel Velis Electro, finding good agreement between experiment and simulation. Finally, an MDO model of a parallel hybrid electric transport aircraft with a liquid-cooled thermal management system is constructed. Sensitivities of aircraft performance with respect to important technologies parameters are computed. This first half introduces the first publicly-available simulation tool that can handle unsteady thermal states and that offers efficient and accurate gradients. The methods are very efficient, enabling users to perform dozens or hundreds of optimization runs in a short amount of time using modest computational resources. Other novel contributions include the first empirical validation of thermal management models for MDO against real flight test data, as well as the only comprehensive look so far at the unsteady thermal management of a transport-scale parallel hybrid aircraft. The second half of the dissertation introduces novel methods for performing high-fidelity shape optimization studies subject to packaging or spatial integration constraints. A new mathematical formulation for generalized packaging constraints is introduced. The constraint formulation is demonstrated on simple aerodynamic shape optimization test cases. Next, a wing design study involving optimal battery packaging is conducted in order to demonstrate the coupling of outer mold line design and propulsion system component design via spatial integration. Finally, a more complex aerostructural optimization involving the wing of a hydrogen aircraft is constructed and solved. These test cases demonstrate the interdisciplinary coupling introduced by packaging constraints, as well as the impact of spatial integration on aircraft performance. This latter half contributes a powerful new way for MDO engineers to pose realistic spatial constraints in their shape optimization problems, thus solving an important practical barrier to the industrial adoption of MDO for certain relevant problems. This work also represents the first time an MDO problem has been posed and solved for an aircraft using hydrogen fuel in the wing. Altogether, this dissertation significantly advances the state of the art in modeling, simulation, and optimization tools for aircraft with electric propulsion architectures and introduces new insights into the design spaces for several diverse aircraft configurations.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169658/1/bbrelje_1.pd

Aprimoramento do poder discriminatório de funções elipsoidais modificadas por cargas fatoriais rotacionadas na formação otimizada de agrupamentos

The technological advent provided the rise of data collection in companies, governments and various industrial segments. In this respect, techniques that seek to perform groupings and discrimination of clusters are widely used in datasets with multiple variables, bringing the need to use specific tools, which contemplate the existing variance-covariance structure. Based on this, this work presents a proposal to improve the discriminatory power of confidence regions in the formation and estimation of optimal clusters, using multivariate and experimental techniques to extract information in an optimized way in correlated datasets. Factor analysis was used as the exploratory multivariate method, tuning the rotation for factor loads through the mixture design, and agglutinating the total variance explained functions by the mean square error afterwards. The optimization of this step is performed through the sequential quadratic programming algorithm. Knowing the optimal scores, a multilevel factorial design is formed to contemplate all combinations of the linkage methods and the types of analysis, seeking to find the parameter that presents the least variability, generating confidence ellipses with better discrimination between groups. A strategy to analyze the levels of agreement and the inversions existence in the formation of clusters is proposed using the Kappa and Kendall indicators. Motivated by the need for strategies to classify substations in the face of voltage sag phenomena, which cause faults in the distribution of electricity, the method was applied to a set of real data, representing the power quality indexes of substations located in southeastern Brazil. Optimum values were found in the factor loads rotation and the parameterization “Wardanalysis of covariance” was defined as the ideal strategies to create the clusters in this dataset. Thus, low variability clusters and precise confidence ellipses were generated to estimate the voltage sag patterns, promoting a better discriminatory power in the clusters’ classification through the regions of confidence. The confirmatory analysis inferred that the “Ward” linkage proved to be the most robust method for this dataset, even under the influence of disturbances in the original data.Agência 1O advento tecnológico proporcionou a ascensão da coleta de dados em empresas, governos e diversos segmentos industriais. Nesse aspecto, técnicas que buscam realizar agrupamentos e discriminação de conglomerados são amplamente empregadas em dados que apresentam múltiplas variáveis, trazendo a necessidade de se utilizar ferramentas específicas, que contemplem a estrutura de variância-covariância existente. Com base nisso, esse trabalho apresenta uma proposta para aprimorar o poder discriminatório de regiões de confiança na formação e estimação de agrupamentos ótimos, utilizando técnicas multivariadas e experimentais para extrair informações de maneira otimizada em conjuntos de dados correlacionados. Como método multivariado exploratório, utilizou-se a análise fatorial, calibrando a rotação de cargas fatoriais através do arranjo de misturas e, em seguida, aglutinando as funções de variância total explicada pelo erro quadrático médio. A otimização dessa etapa é realizada através do algoritmo de programação quadrática sequencial. Conhecendo os escores ótimos, um arranjo fatorial multinível é formado para contemplar todas as combinações dos métodos de ligação e os tipos de análise, buscando encontrar a combinação de parâmetros que apresente a menor variabilidade e que, consequentemente, gere elipses de confiança com melhor discriminação entre os grupos. Uma estratégia para analisar os níveis de concordância e a existência de inversões na formação de clusters é proposta utilizando os indicadores de Kappa e Kendall. Motivado pela necessidade de estratégias para classificar subestações diante de fenômenos de afundamento de tensão, que causam quedas na distribuição de energia elétrica, o método foi aplicado em um conjunto de dados reais, representando os índices de qualidade de energia elétrica de subestações localizadas no sudeste do Brasil. Foram encontrados valores ótimos na rotação das cargas fatoriais e definiu-se a parametrização “Ward e análise de covariância” como as estratégias ideais para criar os clusters nesse conjunto de dados. Assim, gerou-se conglomerados de baixa variabilidade e elipses de confiança precisas para estimar os padrões de afundamentos de tensão, promovendo um melhor poder discriminatório na classificação dos clusters através das regiões de confiança. A análise confirmatória inferiu que o método de ligação “Ward” se mostrou o mais robusto para esse conjunto, mesmo sob influência de perturbações no conjunto original

Non-conventional machining of Al/Sic metal matrix composite

In recent years, aluminum alloy based metal matrix composites (MMC) are gaining importance in several aerospace and automobile applications. Aluminum has been used as matrix material owing to its excellent mechanical properties coupled with good formability. Addition of SiCp as reinforcement in aluminium system improves mechanical properties of the composite. In the present investigation, Al-SiCp composite was prepared by powder metallurgy route. Powder metallurgy homogeneously distributes the reinforcement in the matrix with no interfacial chemical reaction and high localized residual porosity. SiC particles containing different weight fractions (10 and 15 wt. %) and mesh size (300 and 400) is used as reinforcement .Though AlSiC possess superior mechanical properties, the high abrasiveness of the SiC particles hinders its machining process and thus by limiting its effective use in wide areas. Rapid tool wear with poor performance even with advanced expensive tools categories it as a difficult-to-cut material. Non-conventional processes such as electrical discharge machining (EDM) could be one of the best suited method to machine such composites. Four machining parameters such as discharge current (Ip), pulse duration (Ton), duty cycle (),flushing pressure (Fp) and two material properties weight fraction of SiCp and mesh size, and four responses like material removal rate (MRR), tool wear rate (TWR), circularity and surface roughness (Ra) are considered in this study. Taguchi method is adopted to design the experimental plan for finding out the optimal setting. However, Taguchi method is well suited for single response optimization problem. In order to simultaneously optimize multiple responses, a hybrid approach combining principal component analysis (PCA) and fuzzy inference system is coupled with Taguchi method for the optimization of multiple responses. The influence of each parameter on the responses is established using analysis of variances (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, duty cycle and wt% of SiC contribute significantly, where flushing pressure and mesh size of SiCp contribute least to the multiple performance characteristic index

Uma nova técnica de otimização multiobjetivo de modelos probabilísticos multivariados de um processo de soldagem MIG em tubos de alumínio AA6063

To assist in solving the problem of process improvement, restrictions and better welding operation conditions, this work applies the Design of Experiments (DoE), Multiobjective Optimization and Multivariate Statistics methodologies together to provide the necessary support in the management of the production process of MIG welding (Metal Inert Gas), of anti-corona protection rings, manufactured with tubes aluminum alloy 6063 (Aluminum Alloy 6063 - AA6063), T4, 100 mm in diameter and 2 mm thick. This type of process can be controlled by a relatively small number of input variables, that is, the wire feed rate (WF), voltage (V), welding speed (Fr) and the distance from the contact tip to the part of work (Cf). In addition, many outputs can be evaluated and optimized simultaneously. In the present work, the variables of yield (Y), dilution (D), reinforcement index (IR) and penetration index (PI) were investigated. To consider the multivariate nature of the problem, techniques such as Factor Analysis and Bonferroni's simultaneous confidence intervals were applied combined with elliptical constraints. The response variables were modeled mathematically using Poisson regression and the results obtained were satisfactory, since accurate models were achieved. The normal bound intersection method (NBI) produced a set of viable configurations for the input variables that allows the experimenter to find the best configuration of the system in relation to the level of importance of each response. The application demonstrated the optimal parameter solution for the welding process in AA6063 and presented characteristics of minimizing the weld bead geometry to contribute to the better efficiency and effectiveness of the productive management of the welding process. An experimental confirmation procedure was successfully performed to validate the theoretical results obtained in the prediction model.Para auxiliar na resolução do problema de melhoria de processos, restrições e melhores condições de operação de soldagem, este trabalho aplica as metodologias de Design of Experiments (DoE), Otimização Multiobjetivo e a Estatística Multivariada em conjunto para dar o suporte necessário no gerenciamento do processo produtivo de soldagem MIG (Metal Inert Gas), de anéis de proteção anti-corona, fabricado com tubos de alumínio na liga 6063(Aluminum Alloy 6063 - AA6063), T4, de 100 mm de diâmetro e espessura de 2mm de parede. Esse tipo de processo pode ser controlado por um número relativamente pequeno de variáveis de entrada, ou seja, a taxa de alimentação do arame (Wf), a tensão (V), a velocidade de soldagem (Fr) e a distância da ponta de contato à peça de trabalho (Cf). Além disso, muitas saídas podem ser avaliadas e otimizadas simultaneamente. No presente trabalho, as variáveis de rendimento (Y), diluição (D), índice de reforço do cordão (RI) e índice de penetração (PI) foram investigadas. Para considerar a natureza multivariada do problema, técnicas como a Análise Fatorial e os intervalos de confiança simultâneos de Bonferroni foram aplicadas combinadas com restrições elípticas. As variáveis respostas foram modeladas matematicamente por meio de regressão de Poisson e os resultados obtidos foram satisfatórios, uma vez que modelos precisos foram alcançados. O método de intersecção de limite normal (NBI) produziu um conjunto de configurações viáveis para as variáveis de entrada, que permite ao experimentador encontrar a melhor configuração do sistema em relação ao nível de importância de cada resposta. A aplicação demonstrou a solução de parâmetro ótimo para o processo de soldagem em AA6063 e apresentou características de minimização da geometria do cordão de solda para contribuir com a melhor eficiência e eficácia do gerenciamento produtivo do processo de soldagem. Um procedimento experimental de confirmação foi realizado com sucesso para validar os resultados teóricos obtidos no modelo de previsão

Master of Science

thesisTraditional geothermal systems have been limited to geologic systems in which elevated temperatures, abundant water, and high porosity and permeability are found. Engineered geothermal systems (EGS) have been proposed for thermal reservoirs in which insufficient water and/or permeability are present. The EGS model calls for the creation of large fracture networks which penetrate the hot rock resource. These fracture networks are formed by reopening sealed fractures or by creating new fractures using hydraulic fracturing methods common to the oil and gas industry. Application of hydraulic fracturing technologies in geothermal systems and operation of engineered geothermal systems present new issues including the formation of thermal fractures due to temperature differentials and rock shrinkage; and the performance of hydraulic fracturing materials such as proppants under geothermal conditions. The formation of thermal fractures in a geothermal reservoir will be governed by the thermophysical properties of the reservoir rock, including heat capacity, thermal conductivity, coefficient of thermal expansion, etc. Thermal conductivity may be estimated using data obtained from geophysical well logs. Multivariate data analysis methods such as principal components analysis and regression analysis have been used to interpret log data. Significant discrepancies between experimentally-determined thermal conductivity and model-derived thermal conductivity were noted. Possible sources of the discrepancies include rock anisotropy and insufficient data. However, principal components analysis proved to be a valuable resource for data interpretation. The resilience of proppants under geothermal conditions was evaluated. Three proppant types were tested in the presence of water and crushed granite at elevated temperatures for periods up to 11 weeks. Sintered bauxite proppant was found to be susceptible to dissolution in hot geothermal water. Quartz sand proppant and resin-coated bauxite proppant appeared to experience less dissolution. Sintered bauxite and resin-coated bauxite proppants were crush tested both before and after exposure to geothermal conditions and the resistance of the proppants to crushing remained unchanged. Based on the testing regime, resin-coated bauxite proppant appears to be well-suited for use in engineered geothermal systems

A Novel Data-Driven Fault Tree Methodology for Fault Diagnosis and Prognosis

RÉSUMÉ : La thèse développe une nouvelle méthodologie de diagnostic et de pronostic de défauts dans un système complexe, nommée Interpretable logic tree analysis (ILTA), qui combine les techniques d’extraction de connaissances à partir des bases de données « knowledge discovery in database (KDD) » et l’analyse d’arbre de défaut « fault tree analysis (FTA) ». La méthodologie capitalise les avantages des deux techniques pour appréhender la problématique de diagnostic et de pronostic de défauts. Bien que les arbres de défauts offrent des modèles interprétables pour déterminer les causes possibles à l’origine d’un défaut, leur utilisation pour le diagnostic de défauts dans un système industriel est limitée, en raison de la nécessité de faire appel à des connaissances expertes pour décrire les relations de cause-à-effet entre les processus internes du système. Cependant, il sera intéressant d’exploiter la puissance d’analyse des arbres de défaut mais construit à partir des connaissances explicites et non biaisées extraites directement des bases de données sur la causalité des fautes. Par conséquent, la méthodologie ILTA fonctionne de manière analogue à la logique du modèle d'analyse d'arbre de défaut (FTA) mais avec une implication minimale des experts. Cette approche de modélisation doit rejoindre la logique des experts pour représenter la structure hiérarchique des défauts dans un système complexe. La méthodologie ILTA est appliquée à la gestion des risques de défaillance en fournissant deux modèles d'arborescence avancés interprétables à plusieurs niveaux (MILTA) et au cours du temps (ITCA). Le modèle MILTA est conçu pour accomplir la tâche de diagnostic de défaillance dans les systèmes complexes. Il est capable de décomposer un défaut complexe et de modéliser graphiquement sa structure de causalité dans un arbre à plusieurs niveaux. Par conséquent, un expert est en mesure de visualiser l’influence des relations hiérarchiques de cause à effet menant à la défaillance principale. De plus, quantifier ces causes en attribuant des probabilités aide à comprendre leur contribution dans l’occurrence de la défaillance du système. Le modèle ITCA est conçu pour réaliser la tâche de pronostic de défaillance dans les systèmes complexes. Basé sur une répartition des données au cours du temps, le modèle ITCA capture l’effet du vieillissement du système à travers de l’évolution de la structure de causalité des fautes. Ainsi, il décrit les changements de causalité résultant de la détérioration et du vieillissement au cours de la vie du système.----------ABSTRACT : The thesis develops a new methodology for diagnosis and prognosis of faults in a complex system, called Interpretable logic tree analysis (ILTA), which combines knowledge extraction techniques from knowledge discovery in databases (KDD) and the fault tree analysis (FTA). The methodology combined the advantages of the both techniques for understanding the problem of diagnosis and prognosis of faults. Although fault trees provide interpretable models for determining the possible causes of a fault, its use for fault diagnosis in an industrial system is limited, due to the need for expert knowledge to describe cause-and-effect relationships between internal system processes. However, it will be interesting to exploit the analytical power of fault trees but built from explicit and unbiased knowledge extracted directly from databases on the causality of faults. Therefore, the ILTA methodology works analogously to the logic of the fault tree analysis model (FTA) but with minimal involvement of experts. This modeling approach joins the logic of experts to represent the hierarchical structure of faults in a complex system. The ILTA methodology is applied to failure risk management by providing two interpretable advanced logic models: a multi-level tree (MILTA) and a multilevel tree over time (ITCA). The MILTA model is designed to accomplish the task of diagnosing failure in complex systems. It is able to decompose a complex defect and graphically model its causal structure in a tree on several levels. As a result, an expert is able to visualize the influence of hierarchical cause and effect relationships leading to the main failure. In addition, quantifying these causes by assigning probabilities helps to understand their contribution to the occurrence of system failure. The second model is a logical tree interpretable in time (ITCA), designed to perform the task of prognosis of failure in complex systems. Based on a distribution of data over time, the ITCA model captures the effect of the aging of the system through the evolution of the fault causation structure. Thus, it describes the causal changes resulting from deterioration and aging over the life of the system