The thesis presents a framework for integrating models, simulation, and experimental data to diagnose incipient failure modes and prognosticate the remaining useful life of critical components, with an application to the main transmission of a helicopter. Although the helicopter example is used to illustrate the methodology presented, by appropriately adapting modules, the architecture can be applied to a variety of similar engineering systems. Models of the kind referenced are commonly referred to in the literature as physical or physics-based models. Such models utilize a mathematical description of some of the natural laws that govern system behaviors. The methodology presented considers separately the aspects of diagnosis and prognosis of engineering systems, but a similar generic framework is proposed for both. The methodology is tested and validated through comparison of results to data from experiments carried out on helicopters in operation and a test cell employing a prototypical helicopter gearbox. Two kinds of experiments have been used. The first one retrieved vibration data from several healthy and faulted aircraft transmissions in operation. The second is a seeded-fault damage-progression test providing gearbox vibration data and ground truth data of increasing crack lengths. For both kinds of experiments, vibration data were collected through a number of accelerometers mounted on the frame of the transmission gearbox. The applied architecture consists of modules with such key elements as the modeling of vibration signatures, extraction of descriptive vibratory features, finite element analysis of a gearbox component, and characterization of fracture progression. Contributions of the thesis include: (1) generic model-based fault diagnosis and failure prognosis methodologies, readily applicable to a dynamic large-scale mechanical system; (2) the characterization of the vibration signals of a class of complex rotary systems through model-based techniques; (3) a reverse engineering approach for fault identification using simulated vibration data; (4) the utilization of models of a faulted planetary gear transmission to classify descriptive system parameters either as fault-sensitive or fault-insensitive; and (5) guidelines for the integration of the model-based diagnosis and prognosis architectures into prognostic algorithms aimed at determining the remaining useful life of failing components.

The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classication for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to nd a good trade-o between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data, is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines , (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines,(c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

This dissertation proposes several simple, robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters and claw-pole generators with built-in rectifiers. While the flexible energy forms synthesized by power electronic converters greatly enhance the performance and expand the operating region of induction motors and claw-pole generators, they also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. In this dissertation, special characteristics of the connected closed-loop inverter and rectifier have been thoroughly analyzed, with particular interest in their impact on fault behaviors of the induction motor and the claw-pole generator. Based on the findings obtained from the theoretical and experimental analysis, several sensorless thermal, mechanical, and insulation monitoring methods are proposed by smartly utilizing special features and capabilities of the connected power electronic converter. A simple and sensitive stator turn-fault detector is proposed for induction motors fed by closed-loop inverter. In addition, a stator thermal monitoring method based on active DC current injection and direct voltage estimation is also proposed to prevent the closed-loop controlled induction motors from thermally overloading. The performance of both methods is demonstrated by extensive experimental results. Methods to detect serpentine belt slip, serpentine belt defect, rotor eccentricity have been proposed for claw-pole generators using only the available electric sensor information. Methods to detect and protect stator turn faults in claw-pole generators are also presented in this dissertation. Lastly, a novel method to detect the generalized bearing roughness fault is proposed. All the proposed condition monitoring techniques have been validated by experimental results.

CAPES; CNPq / Este trabalho apresenta uma proposta de metodologia para detecção e classificação de falhas em motores de indução trifásicos ligados diretamente à rede elétrica. O método proposto é baseado na análise dos sinais de corrente do estator, com e sem a presença de falhas nos rolamentos, estator e rotor. Um dos efeitos desses tipos de falhas é o aparecimento de componentes de frequência específicas, relacionados à velocidade de rotação da máquina. Os sinais foram analisados usando a decomposição wavelet-packet, que permite a avaliação dos sinais em bandas de frequência de tamanhos variáveis. A partir dessa decomposição, aplicaram-se medidas de previsibilidade, como entropia relativa, potência de previsão e variância de erro normalizada, obtida com a análise de componentes previsíveis. Com essas medidas, foi possível verificar quais componentes da decomposição são mais previsíveis. Neste trabalho, a variância de erro normalizada e a potência de previsão foram utilizadas como entradas para três topologias de redes neurais artificiais classificadoras: perceptron multicamadas, redes de funções de base radial e mapas auto-organizáveis de Kohonen. Foram testados seis diferentes vetores de entrada para as redes neurais, utilizando medidas de previsibilidade e número de elementos dos vetores variados. Os ensaios foram realizados considerando amostras de sinal de diferentes motores, com vários tipos de falha, operando sob diversos regimes de torque e condições de desequilíbrio de tensão. Primeiramente, os sinais foram classificados em dois padrões: com e sem a presença de falhas. Posteriormente, detectou-se o tipo de falha presente nos sinais: rolamento, estator ou rotor. Por último, as amostras foram classificadas dentro do subgrupo de falha em que estavam presentes. / This work presents a methodology for diagnosis and classification of faults in three-phase induction motors connected directly to the power grid. The proposed method is based on the analysis of the stator current signals, with and without the presence of faults in the bearings, stator and rotor. These faults cause the presence of specific frequency components that are related to the machine rotational speed. The signals were analyzed using wavelet-packet decomposition, which allows a multiresolution evaluation of the signals. Using this decomposition, we estimated some predictability measures, such as relative entropy, predictive power and normalized error variance, obtained with the predictability component analysis. With this measures, we verified which were the most predictable components. In this work, normalized error variance and the predictive power were used as inputs to three topologies of artificial neural networks used as classifiers: multilayer perceptron, radial basis function and Kohonen self-organizing maps. We tested six different input vectors to the artificial neural networks, in which we vary the predictability measures and the number of elements of the vectors. The studies were performed considering samples of signals from different motors, with various kinds of faults, working under several load conditions and with voltage unbalance. The signals were firstly classified in two patterns: with and without the presence of faults. After, we detected the kind of fault was present in the signal: bearing, stator or rotor fault. Last, the samples were classified inside the subgroup in which they were.

Muitas habilidades são requeridas aos engenheiros para o sucesso no âmbito do desenvolvimento de produtos. O constante lançamento de novos produtos, associado às crescentes exigências dos clientes e usuários exigem abordagens sistemáticas e proativas da engenharia de projeto. Dado que muitas falhas têm origens prematuras no ciclo de desenvolvimento, ferramentas de identificação de modos de falhas são usadas a fim de prevenir essas ocorrências. Mesmo o método mais tradicional e difundido, a Análise de Modo e Efeitos de Falha (FMEA), não é suficiente por si só. Nesse contexto, esta pesquisa objetivou avaliar o potencial de identificação de falhas potenciais da metodologia Determinação Antecipada de Falha (AFD). Essa ferramenta é derivada da Teoria de Solução Inventiva de Problemas (TRIZ) e recomendada na literatura por encorajar a criatividade sistemática em sua abordagem, porém inexistia a comprovação de sua eficácia. Dessa forma, na metodologia deste trabalho, foram desenvolvidos experimentos por meio de um minicurso teórico-prático sobre ambas ferramentas de predição de falhas (FMEA e AFD) direcionados a alunos de graduação em engenharia para capacitação e aplicação das metodologias. Os estudantes são acessíveis e bastante utilizados nas pesquisas de engenharia de projeto para validações de métodos. Os resultados foram colhidos por meio de formulários ao longo da atividade e embasaram esta pesquisa. Do total de 105 alunos de cinco universidades de Curitiba-PR, a uma significância de 5% e precisão de 9,5%, observou-se que ambas as ferramentas foram bem avaliadas. Comparada à FMEA, a ferramenta AFD foi mais recomendada para o desenvolvimento de produto (59%), enquanto que 61% sugeriram a FMEA para resolução de casos complexos. Embora a AFD tenha sido avaliada como mais robusta (50%) e tão eficiente quanto a FMEA, 60% dos estudantes afirmaram que a FMEA é de mais fácil utilização. Apesar disso, nas aplicações práticas, a AFD mostrou melhores resultados na identificação de falhas e causas potenciais, em 71% dos casos. Esses resultados evidenciaram as potencialidades, e também limitações, da metodologia AFD, mostrando-se um método alternativo e, ainda, pouco explorado na área de desenvolvimento de produto. / Many skills are required for the success of engineers in product development. The constant launch of new products, coupled with the increasing demands of customers and users, requires systematic and proactive approaches to design engineering. Because many faults have premature origins in the development cycle, failure mode identification tools are used to prevent such occurrences. Even the most traditional and widespread method, the Failure Mode and Effects Analysis (FMEA), is not enough by itself. In this context, this research aimed to evaluate the potential of identification of faults by Anticipatory Failure Determination (AFD) methodology. This tool is derived from the Theory of Inventive Problem Solving (TRIZ) and recommended in the literature for encouraging systematic creativity in its approach, but its evidence of effectiveness was inexistent. Thus, in the methodology of this work, experiments were developed through a theoretical-practical mini-course on both fault prediction tools (FMEA and AFD) directed to undergraduate students in engineering for training and application of methodologies. Students are accessible and widely used in design engineering surveys for method validations. The results were collected through forms throughout the activity and supported this research. From the total of 105 students from five universities in Curitiba-PR, at a significance of 5% and accuracy of 9.5%, it was observed that both tools were well evaluated. Compared to FMEA, the AFD tool was more recommended for product development (59%), while 61% suggested FMEA for complex case resolution. Although AFD was rated as more robust (50%) and as effective as FMEA, 60% of students said that FMEA is easier. Nevertheless, in practical applications, AFD showed a better results in the identification of failures and potential causes in 71% of the cases. These results evidenced the potentialities, and also counterparts, of the AFD methodology, showing an alternative method and, still, little explored in the area of product development.

Muitas habilidades são requeridas aos engenheiros para o sucesso no âmbito do desenvolvimento de produtos. O constante lançamento de novos produtos, associado às crescentes exigências dos clientes e usuários exigem abordagens sistemáticas e proativas da engenharia de projeto. Dado que muitas falhas têm origens prematuras no ciclo de desenvolvimento, ferramentas de identificação de modos de falhas são usadas a fim de prevenir essas ocorrências. Mesmo o método mais tradicional e difundido, a Análise de Modo e Efeitos de Falha (FMEA), não é suficiente por si só. Nesse contexto, esta pesquisa objetivou avaliar o potencial de identificação de falhas potenciais da metodologia Determinação Antecipada de Falha (AFD). Essa ferramenta é derivada da Teoria de Solução Inventiva de Problemas (TRIZ) e recomendada na literatura por encorajar a criatividade sistemática em sua abordagem, porém inexistia a comprovação de sua eficácia. Dessa forma, na metodologia deste trabalho, foram desenvolvidos experimentos por meio de um minicurso teórico-prático sobre ambas ferramentas de predição de falhas (FMEA e AFD) direcionados a alunos de graduação em engenharia para capacitação e aplicação das metodologias. Os estudantes são acessíveis e bastante utilizados nas pesquisas de engenharia de projeto para validações de métodos. Os resultados foram colhidos por meio de formulários ao longo da atividade e embasaram esta pesquisa. Do total de 105 alunos de cinco universidades de Curitiba-PR, a uma significância de 5% e precisão de 9,5%, observou-se que ambas as ferramentas foram bem avaliadas. Comparada à FMEA, a ferramenta AFD foi mais recomendada para o desenvolvimento de produto (59%), enquanto que 61% sugeriram a FMEA para resolução de casos complexos. Embora a AFD tenha sido avaliada como mais robusta (50%) e tão eficiente quanto a FMEA, 60% dos estudantes afirmaram que a FMEA é de mais fácil utilização. Apesar disso, nas aplicações práticas, a AFD mostrou melhores resultados na identificação de falhas e causas potenciais, em 71% dos casos. Esses resultados evidenciaram as potencialidades, e também limitações, da metodologia AFD, mostrando-se um método alternativo e, ainda, pouco explorado na área de desenvolvimento de produto. / Many skills are required for the success of engineers in product development. The constant launch of new products, coupled with the increasing demands of customers and users, requires systematic and proactive approaches to design engineering. Because many faults have premature origins in the development cycle, failure mode identification tools are used to prevent such occurrences. Even the most traditional and widespread method, the Failure Mode and Effects Analysis (FMEA), is not enough by itself. In this context, this research aimed to evaluate the potential of identification of faults by Anticipatory Failure Determination (AFD) methodology. This tool is derived from the Theory of Inventive Problem Solving (TRIZ) and recommended in the literature for encouraging systematic creativity in its approach, but its evidence of effectiveness was inexistent. Thus, in the methodology of this work, experiments were developed through a theoretical-practical mini-course on both fault prediction tools (FMEA and AFD) directed to undergraduate students in engineering for training and application of methodologies. Students are accessible and widely used in design engineering surveys for method validations. The results were collected through forms throughout the activity and supported this research. From the total of 105 students from five universities in Curitiba-PR, at a significance of 5% and accuracy of 9.5%, it was observed that both tools were well evaluated. Compared to FMEA, the AFD tool was more recommended for product development (59%), while 61% suggested FMEA for complex case resolution. Although AFD was rated as more robust (50%) and as effective as FMEA, 60% of students said that FMEA is easier. Nevertheless, in practical applications, AFD showed a better results in the identification of failures and potential causes in 71% of the cases. These results evidenced the potentialities, and also counterparts, of the AFD methodology, showing an alternative method and, still, little explored in the area of product development.

Orientadores: Elias Basile Tambourgi, Sergio Ricardo Lourenço / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica / Made available in DSpace on 2018-08-11T04:10:47Z (GMT). No. of bitstreams: 1 BertoJunior_CarlosAntonio_M.pdf: 2955531 bytes, checksum: cfd5bb39409050bc06069b54171a03f9 (MD5) Previous issue date: 2008 / Resumo: O gás natural possui alguns contaminantes que, além de serem corrosivos, comprometem a qualidade para o consumo. Dessa forma, a condensação de água residual no gás pode iniciar um processo corrosivo localizado, que pode acarretar prejuízo à estrutura dos gasodutos. Devido à grande extensão dos dutos os corrosivos comprometem a qualidade do gás e causam grandes transtornos de ordem operacional. Para avaliar a redução da espessura da parede metálica do duto, proveniente de efeitos corrosivos, e identificar fissuras e outras nãoconformidades, é fundamental que seja feito o monitoramento contínuo e que se utilizem técnicas e métodos de manutenção preditiva. Atualmente as técnicas adotadas para tal avaliação consistem na inclusão de um corpo de prova, conhecido como pipeline inspection gauge (PIG), com varredura por meio de ultra-som, termografia, sensores ópticos, sensores de efeito Hall e sensores para análise de resistência elétrica, além de levantamentos de campo especiais realizados sobre a superfície do solo. Assim, o presente trabalho teve como norteador a otimização do processo de detecção, com vistas à redução de custos e precisão na identificação das falhas. Para tal, foi implementado um PIG autônomo para o monitoramento contínuo da região interna dos dutos dotado de câmeras infra-vermelho, o que diferencia este equipamento dos atuais para o mesmo fim. As câmeras fornecem imagens que são processadas digitalmente e gravadas em uma memória não-volátil presente no equipamento. Um software é utilizado para verificar as imagens e, ao mesmo tempo, identificar as não-conformidades presentes. Estas informações serão utilizadas como orientador na tomada de decisão acerca do processo de manutenção que deverá ser utilizado para a solvência dos problemas encontrados / Abstract: Natural gas has hazardous contaminants that, besides being corrosive, compromise the quality for the consumption. For in such a way the present residual water condensation in the gas can start a local corrosive process, which could cause damage to the structure of the gas ducts. Due to the great length of a gas duct, corrosive components decrease its quality and cause great operational problems. To evaluate the reduction of the metallic wall thickness for duct, proceeding from corrosive effect, and to identify to fictions and other notconformity, it is basic that the continuous monitoring is made and that techniques and methods of predictive maintenance are used. Currently the techniques adopted for such evaluation consist the inclusion of a body test, known as pipeline inspection gauge (PIG), with sweepings by means of ultrasound, thermograph, optical sensor, hall-effect and analysis of electric resistance, beyond carried through special surveys of field on the surface of the ground. Thus, the present work had as optimization of the detention process, with sights to the reduction of costs and precision in the identification of imperfections. For such, a PIG independent for the continuous monitoring for internal region in ducts was implemented endowed with cameras infra-red, what it the same differentiates this equipment of the current ones for end. The cameras supply recorded images that are processed digitally and in a present not-volatile memory in the equipment. A software is used to verify the images and, at the same time, to identify to notconformity gifts. These information will be used as orienting in the taking of decision concerning the maintenance process that will have to be used for the solution of the joined problems / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química

Orientador: Janito Vaqueiro Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-26T00:41:26Z (GMT). No. of bitstreams: 1 Foresti_BernardoPereira_M.pdf: 7267999 bytes, checksum: 1a200bffc0e39d2a529a64080b7ddad0 (MD5) Previous issue date: 2014 / Resumo: Neste trabalho buscou-se desenvolver uma metodologia que se valendo de dados de vibração estrutural de carcaça e da operação de uma bomba do bombeio centrífugo submerso (BCS) fosse capaz de identificar parâmetros da operação deste tipo de máquina, tais como: vazão mássica de líquido e gás, diferença de pressão, eficiência e potência mecânica. Para isso foram adaptados os seguintes métodos de diagnóstico de falhas: Método Baseado na Densidade Espectral de Potência, Método Baseado na Função Resposta em Frequência, Método Baseado na Medida de Coerência, Métodos Baseados nos Parâmetros do Modelo (Geométrico e Não-geométrico), Métodos Baseados nos Resíduos do Modelo (Baseado na Variância e Auto-covariância dos Resíduos) e Método Baseado em Modelos Funcionais. Tais métodos requerem a organização de um banco de dados, na fase de levantamento de referências utilizado para comparação com dados obtidos na fase de inspeção, visando à detecção, identificação e estimação da magnitude das falhas e defeitos, conceitos adaptados para o problema apresentado neste trabalho. A metodologia foi aplicada a dois casos: o primeiro, numérico, baseado em dados obtidos da simulação de um sistema de três graus de liberdade foi utilizado para operacionalização da metodologia e antecipação de problemas e dificuldades em sua aplicação. O segundo, experimental, principal foco deste trabalho, baseado em uma bomba utilizada no bombeio centrífugo submerso. Para aplicação da metodologia ao caso experimental, foi elaborado experimento utilizando uma bomba do BCS de quatro estágios instrumentada operando com escoamento bifásico ar-água em diferentes proporções. Resultados indicaram bom desempenho na detecção do tipo de escoamento (monofásico/bifásico), na identificação da vazão mássica de gás escoado e na estimação da vazão de líquido transportado pelo BCS / Abstract: In this research a method using structural vibration and operational data of a pump module normally used with and electrical submersible pump (ESP) has been developed to identify operational parameters, such as: liquid and gas flow rate, differential pressure, efficiency and shaft power. To this end, the following time-series fault detection and identification (FDI) methods were adapted: Power Spectral Density-based Method, Frequency Response Function-based Method, Coherence Measure-based Method, Parameter-based Method (Geometric and Non-geometric), Residual-based Methods (Residual Variance and Residual Uncorrelatedness) and Functional Model-based Method. For FDI, the methods require the set-up of a data base, in the baseline phase used for comparison with data obtained during inspection. The methodology was applied for two cases: A numerical problem based on a three degrees of freedom system, aiming at making functional the programs used and anticipating problematic issues and experimental data from a real ESP pump module, main focus of this work. The experiment consists of measuring structural vibration, and operational data of an ESP pump while varying liquid and gas flow rates keeping shaft speed and suction pressure constant. Results have indicated successful detection of flow type (monophasic/biphasic), identification of the gas flow and estimation of the liquid flow pumped by the ESP pump / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

Orientador: Auteliano Antunes dos Santos Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-20T21:43:45Z (GMT). No. of bitstreams: 1 Leao_RodrigoJunqueira_M.pdf: 11585473 bytes, checksum: eb36aabbabd372414779881f910455a8 (MD5) Previous issue date: 2012 / Resumo: Materiais compósitos são cada vez mais utilizados na indústria aeroespacial, por apresentarem baixa relação entre massa específica e resistência mecânica. Para a realização de ensaios não destrutivos utilizando o ultrassom, faz-se necessário conhecer a velocidade com que o som se propaga através desses materiais. Nem sempre é possível desenvolver protótipos reais durante o desenvolvimento de um projeto, por limitações construtivas e de custo; modelos virtuais são, pois, necessários. O objetivo deste trabalho é desenvolver modelos virtuais para avaliar a propagação de ondas em compósitos e compará-los com resultados experimentais. Uma liga de alumínio é usada inicialmente, de forma a calibrar o modelo e configurar alguns parâmetros de simulação. O material composto analisado é um laminado unidirecional, fabricado a partir de 97 camadas de material pré-impregnado (AS4/8552) da Hexcel¿. Utiliza-se o método dos elementos finitos para simular a geração, propagação e recepção de ondas ultrassônicas no modelo. O foco do estudo são ondas longitudinais de volume, embora a geração de ondas longitudinais criticamente refratadas (Lcr) também seja demonstrada. A razão é que o estudo é parte de uma pesquisa sobre o desenvolvimento de técnicas ultrassônicas para a medição de tensões em compósitos, utilizando Acustoelasticidade. A fim de permitir a medição da velocidade da onda ultrassônica em diferentes orientações, foi fabricado um corpo de prova em formato de prisma de base poligonal de 24 lados. O modelo numérico desenvolvido considera o caso ideal, onde as lâminas são perfeitamente coladas umas nas outras e não há problemas como delaminação ou vazios. Um modelo simplificado de cada lâmina foi admitido, de modo a utilizar uma malha menos refinada nas simulações e reduzir o gasto computacional. A fração volumétrica de reforço e matriz foi mantida. Um pulso de 1 MHz foi inserido no modelo e as discretizações no tempo e no espaço foram escolhidas de forma coerente. Simulações para o caso de 0º e 90º foram feitas e um modelo para os outros ângulos de orientação foi proposto. Os resultados mostram-se satisfatórios e indicam que, no futuro, o modelo simplificado adotado poderá ser estendido, levando em conta não conformidades e uma distribuição mais heterogênea das fibras, permitindo o desenvolvimento de ferramentas de inspeção aperfeiçoadas / Abstract: The use of composite materials in the aerospace industry is increasing due to its low ratio between density and mechanical strength. To perform non-destructive testing using ultrasound, it is necessary to know the sound velocity in these materials. It is not always possible to manufacture physical prototypes during the development of a project because of time, construction limitation and cost; virtual models are therefore needed. The objective of this work is to develop virtual models to evaluate the wave propagation in composites and compare them with experimental results. Initially, an aluminum alloy is used in order to calibrate the model and configure some simulation parameters. The composite material analyzed is a unidirectional laminate, made from 97 layers of prepreg material (AS4/8552) from Hexcel¿. We use the finite element method to simulate the generation, propagation and reception of ultrasonic waves in the model. The focus of this study is the generation of longitudinal bulk waves, although the generation of Critically Refracted Longitudinal (Lcr) waves is also demonstrated. The reason is that the study is part of an ongoing research project on the development of ultrasonic techniques for measuring residual stress in composites, using acoustoelasticity. To enable the measurement of the ultrasonic wave velocity in different orientations, we manufactured a specimen in a prismatic shape (24-sided polygonal base). The numerical model consists of the ideal case, where the different materials are completely attached to each other and there are no problems such as delamination or voids. A simplified model of each layer was admitted, to use a less refined mesh in the simulations and reduce the computational cost. The volume fraction of reinforcement and matrix was maintained. A pulse of 1 MHz was inserted into the model and the discretization both in time and space was chosen consistently. Simulations for the case of 0° and 90° were made and a model for the other orientations was proposed. The results prove to be satisfactory and indicate that in the future, the simplified model adopted could be extended, taking into account nonconformities and a more heterogeneous distribution of the fibers, allowing the development of improved inspection tools / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica