Orientador: Paulo Sollero / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-18T19:31:17Z (GMT). No. of bitstreams: 1 QuispeRodriguez_Rene_M.pdf: 18563106 bytes, checksum: 3b10d1a137086184fd58033d26af0222 (MD5) Previous issue date: 2011 / Resumo: A aplicação de adesivos para união de materiais cresceu consideravelmente no decorrer dos últimos anos, sendo que tal crescimento se deve aos benefícios proporcionados pelos adesivos, quando comparados aos métodos tradicionais de união, como solda ou utilização de parafusos. Na indústria, características como fácil aplicabilidade, melhor distribuição de tensões, prolongada vida útil, maior absorção de impactos e vibrações, menores custos de produtos e processos, tornam a utilização de adesivos soluções interessantes e competitivas. Existe então uma necessidade especifica de análise e da criação de ferramentas que ajudem no projeto de juntas coladas. O presente trabalho visa suprir em certa forma essa necessidade, mediante o estudo e implementação de modelos analíticos e critérios de falha. Para a validação numérica foi utilizado o método dos elementos finitos (MEF), mediante o uso do software comercial ABAQUS. Os modelos analíticos, numéricos e critérios de falha foram introduzidos em um software de fácil uso, denominado "KISPEO". Este software foi programado em sua maior parte mediante o aplicativo GUI (Graphical User Interface) do MATLAB. O software, que conta com interfaces amigáveis, é focado na análise das distribuições de tensões em juntas coladas de sobreposição simples (SLJ). Os modelos implementados no presente trabalho foram logo validados com ensaios experimentais normalizados segundo a norma ASTM (American Society for Testing and Materials) / Abstract: Application of adhesives in bonded joints has increased considerably over recent years. This growth is due to the benefits provided by adhesives, when compared to conventional joining methods, like rivets, bolts or welding. In the industry, characteristics as easy applicability, better stress distributions, improved service life, better impact and vibration absorption, less process and product costs, make adhesives an interesting and competitive option. Therefore, there is a specific need for analysis and design tools that can provide physical insight and accurate results for bonded joint applications. The present work aims to fulfill partially this need, studying and implementing several analytical models and failure criteria for bonded joints. For the numerical validation was utilized the Finite Element Method (FEM), using the commercial software ABAQUS. Analytical methods, numerical models and failure criteria were introduced in a user-friendly software, named "KISPEO". This software was implemented using the applicative GUI (Graphical User Interface) of MATLAB. The software, which features graphical interfaces, is focused in stress distribution and failure criteria analysis of Single Lap Joints (SLJ). Finally, implemented models were validated with experimental tests according to the ASTM (American Society for Testing and Materials) standard / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

The emergence of complex and autonomous systems, such as modern aircraft, unmanned aerial vehicles (UAVs) and automated industrial processes is driving the development and implementation of new control technologies aimed at accommodating incipient failures to maintain system operation during an emergency. The motivation for this research began in the area of avionics and flight control systems for the purpose to improve aircraft safety. A prognostics health management (PHM) based fault-tolerant control architecture can increase safety and reliability by detecting and accommodating impending failures thereby minimizing the occurrence of unexpected, costly and possibly life-threatening mission failures; reduce unnecessary maintenance actions; and extend system availability / reliability. Recent developments in failure prognosis and fault tolerant control (FTC) provide a basis for a prognosis based reconfigurable control framework. Key work in this area considers: (1) long-term lifetime predictions as a design constraint using optimal control; (2) the use of model predictive control to retrofit existing controllers with real-time fault detection and diagnosis routines; (3) hybrid hierarchical approaches to FTC taking advantage of control reconfiguration at multiple levels, or layers, enabling the possibility of set-point reconfiguration, system restructuring and path / mission re-planning. Combining these control elements in a hierarchical structure allows for the development of a comprehensive framework for prognosis based FTC. First, the PHM-based reconfigurable controls framework presented in this thesis is given as one approach to a much larger hierarchical control scheme. This begins with a brief overview of a much broader three-tier hierarchical control architecture defined as having three layers: supervisory, intermediate, and low-level. The supervisory layer manages high-level objectives. The intermediate layer redistributes component loads among multiple sub-systems. The low-level layer reconfigures the set-points used by the local production controller thereby trading-off system performance for an increase in remaining useful life (RUL). Next, a low-level reconfigurable controller is defined as a time-varying multi-objective criterion function and appropriate constraints to determine optimal set-point reconfiguration. A set of necessary conditions are established to ensure the stability and boundedness of the composite system. In addition, the error bounds corresponding to long-term state-space prediction are examined. From these error bounds, the point estimate and corresponding uncertainty boundaries for the RUL estimate can be obtained. Also, the computational efficiency of the controller is examined by using the number of average floating point operations per iteration as a standard metric of comparison. Finally, results are obtained for an avionics grade triplex-redundant electro-mechanical actuator with a specific fault mode; insulation breakdown between winding turns in a brushless DC motor is used as a test case for the fault-mode. A prognostic model is developed relating motor operating conditions to RUL. Standard metrics for determining the feasibility of RUL reconfiguration are defined and used to study the performance of the reconfigured system; more specifically, the effects of the prediction horizon, model uncertainty, operating conditions and load disturbance on the RUL during reconfiguration are simulated using MATLAB and Simulink. Contributions of this work include defining a control architecture, proving stability and boundedness, deriving the control algorithm and demonstrating feasibility with an example.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references.

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This research develops a fault diagnosis method for complex systems in the presence of uncertainties and possibility of multiple solutions. Fault diagnosis is a challenging problem because data used in diagnosis contain random errors and often systematic errors as well. Furthermore, fault diagnosis is basically an inverse problem so that it inherits unfavorable characteristics of inverse problems: The existence and uniqueness of an inverse solution are not guaranteed and the solution may be unstable. The weighted least squares method and its variations are traditionally used for solving inverse problems. However, the existing algorithms often fail to identify multiple solutions if they are present. In addition, the existing algorithms are not capable of selecting variables systematically so that they generally use the full model in which may contain unnecessary variables as well as necessary variables. Ignoring this model uncertainty often gives rise to, so called, the smearing effect in solutions, because of which unnecessary variables are overestimated and necessary variables are underestimated. The proposed method solves the inverse problem using Bayesian inference. An engineering system can be parameterized using state variables. The probability of each state variable is inferred from observations made on the system. A bias in an observation is treated as a variable, and the probability of the bias variable is inferred as well. To take the uncertainty of model structure into account, multiple Bayesian models are created with various combinations of the state variables and the bias variables. The results from all models are averaged according to how likely each model is. Gibbs sampling is used for approximating updated probabilities. The method is demonstrated for two applications: the status matching of a turbojet engine and the fault diagnosis of an industrial gas turbine. In the status matching application only physical faults in the components of a turbojet engine are considered whereas in the fault diagnosis application sensor biases are considered as well as physical faults. The proposed method is tested in various faulty conditions using simulated measurements. Results show that the proposed method identifies physical faults and sensor biases simultaneously. It is also demonstrated that multiple solutions can be identified. Overall, there is a clear improvement in ability to identify correct solutions over the full model that contains all state and bias variables.

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2008) Includes bibliographical references.

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 123-128). Also available in electronic format on the Internet.

Fundação Araucária, CNPq / O motor de indução trifásico ocupa uma posição de destaque na produção de força eletromotriz e isso o torna vastamente utilizado em aplicações industriais. Consequentemente, também fica submetido às condições de funcionamento e manutenção das máquinas como um todo, bem como das falhas que os mesmos estão sujeitos. Assim, este trabalho propõe um método alternativo aos tradicionais para detecção de falhas em rolamentos de motores de indução trifásicos ligados diretamente a rede elétrica. Os objetivos consistem na utilização de uma abordagem neural capaz de classificar a existência de falha de rolamento com um alto percentual de acerto. Analisando para isto, no domínio do tempo, um semiciclo das tensões de alimentação e das correntes de estator dos motor em estudo. A proposta é validada através de ensaios experimentais num computador e de forma on-line embarcada num DSP. Como conseqüência do trabalho tem-se a criação de um banco de dados de falhas, com mais de mil ensaios envolvendo as principais falhas encontradas em motores de indução trifásicos. Estes ensaios são realizados contemplando as condições de desbalanço de tensão de alimentação e com várias situações de carga mecânica no eixo da máquina. / The three phase induction motor occupies a prominent position in the production of electromotive force and this makes it widely used in industrial applications. Consequently, it is also subjected to the conditions of operation and maintenance of the machines as a whole, as well as faults which they are subject. Thus, this paper proposes an alternative method to traditional in fault detection in bearing of induction motors connected directly to the power grid. The objectives consist in using a neural approach able to classify the existence of bearing fault with a high percentage of correct. Analyzing for this, in the time domain, one half cycle of the voltages and currents of stator the motor in study. The proposal is validated through experimental tests on a computer and monitoring on-line embedded in a DSP. As a result, the work has the creation of a database of failure, with more than a thousand trials involving the main flaws found in three phase induction motors. These tests are performed considering the conditions of voltage supply unbalanced and with several situations of mechanical load on the machine shaft.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná / O motor de indução trifásico do tipo gaiola de esquilo é a principal máquina de conversão eletromecânica devido a sua robustez e facilidade de manutenção, sendo indispensável nos processos produtivos industriais. Por sua grande importância, pesquisas na área de identificação de falhas são realizadas constantemente com o objetivo de diminuir as taxas de manutenções corretivas e permitir uma manutenção programada, diminuindo perdas no processo produtivo, decorrentes de paradas indesejadas. Com esse propósito, o presente trabalho propõe o estudo e desenvolvimento de uma metodologia alternativa que utiliza a Evolução Diferencial para identificação de falhas de curto-circuito de estator em motores de indução conectados diretamente à rede elétrica, por meio dos sinais de tensão e corrente aquisitados no domínio do tempo. O algoritmo de Evolução Diferencial é utilizado para estimar os parâmetros elétricos do motor de indução, a partir do modelo do circuito elétrico equivalente e a identificação da falha ocorre a partir do cálculo da variação percentual da indutância de magnetização estimada com o motor sem falhas. A base de dados utilizada para o trabalho foi obtida por meio de experimentos laboratoriais realizados com dois motores diferentes de 1 CV e um motor de 2 CV, sob condições de variação de conjugado, tensões equilibradas e desequilibradas. / The squirrel cage three-phase induction motor is the main electromechanical conversion machine due to its robustness and easy maintenance, therefore it is indispensable in the industrial production processes. Due to its great importance, surveys related to its fault identification are conducted constantly, in order to reduce the corrective maintenance rates and allow a scheduled maintenance, reducing the losses in the production process, due to unexpected stops. With this purpose, this work proposes the study and development of an alternative methodology based on Differential Evolution algorithm to identify stator short-circuit failures in induction motors connected directly on the electric grid, through its voltage and current signals acquired in the time domain. This Differential Evolution algorithm is used to estimate the induction motor electric parameters based on its equivalent electric circuit model and the fault identification occurs in result of the calculation of the estimated magnetization inductance percentage variation considering a healthy motor. The database used for this work was obtained through laboratory experiments performed with two different types of 1 CV motor and a 2 CV motor under different conditions of torque variation and unbalanced voltages.