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Cette thèse s'inscrit dans le cadre d'une collaboration avec EADS Innovation Works et AIRBUS. L'objectif des travaux est d'identifier les modes d'endommagements possibles d'éléments de structure métalliques d'aéronefs développés en service et d'en comprendre les mécanismes et les effets sur les propriétés des matériaux afin de contribuer au développement d'une méthode de contrôle non destructif innovante. Le matériau sélectionné est un alliage d'aluminium 2024-T351, l'un des matériaux constitutifs de la voilure et du fuselage d'avions civils. Les modes d'endommagement étudiés sont la corrosion et le vieillissement microstructural. La première partie de ces travaux est consacrée à l'analyse de l'influence des conditions d'exposition au milieu corrosif sur le développement de la corrosion intergranulaire et à l'identification des mécanismes de dégradation associés et de leurs cinétiques. Des conditions d'exposition originales alternant des phases d'immersion et d'émersion à différentes températures ont été explorées dans la mesure où elles semblent particulièrement représentatives des conditions d'exposition réelles. Les mécanismes proposés pour comprendre l'endommagement observé dans certaines de ces conditions d'exposition au milieu corrosif, impliquent un phénomène apparenté à de la fragilisation par l'hydrogène, phénomène qui n'est, à l'heure actuelle, pas encore reconnu pour les alliages d'aluminium de la série 2xxx. L'influence de l'hydrogène sur les propriétés physico-chimiques et mécaniques du matériau est donc étudiée dans la seconde partie de ces travaux. Enfin, l'influence d'un vieillissement microstructural sur les propriétés de l'alliage ainsi que les couplages possibles entre vieillissement microstructural et phénomènes de corrosion sont abordés dans une dernière partie. L'ensemble des résultats obtenus permet de révéler des pistes pour développer une méthode CND innovante permettant la caractérisation physique in-situ du niveau d'endommagement à l'échelle locale d'éléments de structures en alliages d'aluminium. / In the framework of a collaborative program research with EADS Innovation Works and AIRBUS, this work aims to identify the possible sources of in service damage of pieces of aircraft structure and the impact of these degradations on the mechanical properties of the materials. The results obtained allow describing the mechanisms involved and their kinetics, in order to contribute to the development of an innovative non destructive method. The material selected is the aluminum alloy 2024-T351, one of the constitutive materials for skin and wings of civil aircraft. For this study, the corrosion and the microstructural evolutions have been selected among the possible causes of degradation identified. The first part of this study is dedicated to the analysis of the influence of exposure conditions to the aggressive media on the development of intergranular corrosion and to the identification of the corrosion mechanisms involved and theirs kinetics. Original exposure conditions, alternating immersion steps in corrosive media and emersion steps in air at different temperatures, have been used insofar as these conditions have been estimated as representative of real exposure conditions. For some exposure conditions, the proposed mechanism to explain the damage observed implies a phenomenon related to hydrogen embrittlement which is, at the moment, not well recognized for aluminum alloys of the 2xxx series. The influence of hydrogen on the mechanical and physicochemical properties of the 2024 is so treated in the second part of this study. Finally, the impact of microstructural ageing as well as its possible coupling with corrosion is discussed in the last part. The whole results obtained allow the identification of leads to develop an innovative non destructive method allowing the physical characterization of local damage of aluminum alloys used to build civil aircrafts.

Orientador: Jose Maria Campos dos Santos / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-07-31T20:57:31Z (GMT). No. of bitstreams: 1 Ferraz_FabioGuilherme_M.pdf: 9315977 bytes, checksum: 2c619b06df862551ab474095f22421cc (MD5) Previous issue date: 2001 / Resumo: Este trabalho explora o uso de métodos de Síntese Modal dos Componentes (SMC), a Teoria da Perturbação por Mínimo Rank (MRPT) e o Algoritmo de Realização de vetores de Ritz (RRA) como uma ferramenta para a detecção de danos estruturais. A SMC consiste em modelar separadamente componentes individuais de uma estrutura e então acoplá-Ios num sistema único. Os métodos de SMC podem ser generalizados para se permitir o uso de outros vetores, tais como os vetores de Ritz em substituição aos modos normais. Os vetores de Ritz são obtidos a partir de relações de recorrência, e representam uma ótima base de vetores como alternativa aos modos naturais de vibrar dos componentes. O MRPT é um método de detecção de danos baseado em modelo, que usa o fato de que um dano discreto pode ser manifestado num modelo de elementos finitos (MEF) como uma pequena perturbação no rank das matrizes que compõe as propriedades da estrutura. O RRA converte as matrizes em tempo discreto identificadas pelo algoritmo ERA para o tempo contínuo, a partir das quais pode-se extrair os vetores de Ritz experimentais usados para se complementar a matriz de forças dinâmicas residuais na formulação do MRPT. Através do modelo sem danos sintetizado, e do modelo medido com danos, o MRPT com RRA é usado para se detectar a localização do dano e sua extensão, ou no mínimo o componente que contém o dano. Diferentes considerações de modelagem subestruturada são exploradas na detecção de danos usando exemplos numéricos. Em particular um MEF de vigas da estação espacial internacional é implementado e os efeitos de danos localizados em diferentes componentes são investigados / Abstract: This work explores the use of Component Mode Synthesis methods (CMS), Minimum Rank Perturbation Theory (MRPT), and Ritz Realization AIgorithm (RRA) as a structural damage detection tooI. The CMS consists in to modeling individual components of a structure separately and then to couple them to form an assembled system. CMS methods can be generalized to allow the use of other vectors, like Ritz vectors, rather than normal modes. The Ritz vectors are obtained from recurrence relations, and represent a suitable basis vectors as an alternative to component vibration modes. MRPT is a model-based damage detection method, which utilizes the fact that discrete damage is manifested in a structural finite element model (FEM) as a low rank perturbation to the structural property matrices. RRA converts the discrete time system matrices identified by Eigensystem Realization AIgorithm (ERA) to continuous time system matrices, from which one can extract the experimental Ritz vectors used to complement the matrix of dynamic residual forces in the MRPT formulation. With the coupled healthy model and the measured damaged model, the MRPT with RRA is used to detect the damage location and his extent, or at least the component that contains the damage. Different substructure modeling assumptions on damage detectability are explored using numerical examples. In particular a beam FEM of the international space station structure is implemented and the effects of different localized component damages are investigated. / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

Structural Health Monitoring (SHM) techniques are widely used in a number of Non – destructive Evaluation (NDE) applications. There is a need to develop effective techniques for SHM, so that the safety and integrity of the structures can be improved. Two most widely used SHM methods for plates and rods use either the spectrum of the impedances or monitor the propagation of lamb waves. Piezoelectric wafer – active sensors (PWAS) were used for excitation and sensing. In this study, surface response to excitation (SuRE) and Lamb wave propagation was monitored to estimate the integrity of the round objects including the pipes, tubes and cutting tools. SuRE obtained the frequency response by applying sweep sine wave to surface. The envelope of the received signal was used to detect the arrival of lamb waves to the sensor. Both approaches detect the structural defects of the pipes and tubes and the wear of the cutting tool.

Detecção de dano em estruturas de engenharia de grandes dimensões através da análise de suas características dinâmicas envolve diversos campos de estudo. O primeiro deles trata da identificação dos parâmetros modais da estrutura, uma vez que executar testes de vibração livre em tais estruturas não é uma tarefa simples, necessita-se de um método robusto que seja capaz de identificar os parâmetros modais dessa estrutura a ações ambientais, campo esse chamado de análise modal operacional. Este trabalho trata do problema de detecção de dano em estruturas que possam ser representadas através de modelos em pórticos planos e vigas e que estejam submetidos à ação de vibrações ambientais. A localização do dano é determinada através de um algoritmo de otimização conhecido como Backtracking Search Algorithm (BSA) fazendo uso de uma função objetivo que utiliza as frequências naturais e modos de vibração identificados da estrutura. Simulações e testes são feitos a fim de verificar a concordância da metodologia para ambos os casos. Para as simulações, são utilizados casos mais gerais de carregamentos dinâmicos, e dois níveis de ruído (3% e 5%) são adicionados ao sinal de respostas para que esses ensaios se assemelhem aos ensaios experimentais, onde o ruído é inerente do processo. Já nos ensaios experimentais, apenas testes de vibração livre são executados. Diversos cenários de dano são propostos para as estruturas analisadas a fim de se verificar a robustez da rotina de detecção de dano. Os resultados mostram que a etapa de identificação modal estocástica através do método de identificação estocástica de subespaço (SSI) teve ótimos resultados, possibilitando, assim, a localização da região danificada da estrutura em todos os casos analisados. / Damage detection in large dimensions engineering structures through the analysis of their dynamic characteristics involves several fields. The first one deals with the structure modal identification parameter, since running free vibration tests in such structures is not a simple task, robust methods are needed in order to identify the modal parameters of this structure under ambient vibrations, this field is known as operational modal analysis. This work deals with the problem of damage detection in structures under ambient vibrations that can be represented by FEM using frame and beam elements. The damage location is determined through an optimization algorithm know as Backtracking Search Algorithm (BSA). It uses as objective function the identified natural frequencies and modes of vibration of the structure. Numerical and experimental tests are performed to assess the agreement of the methodology for both cases. For the numerical tests, more general cases of dynamic loads are used, and two noise levels (3% and 5%) are added to the response signal to assessing the robustness of the methodology close to the field conditions, in which noise is inherent of the process. In the experimental tests, only free vibration tests are performed. Several damage scenarios are proposed for the analyzed structures to check the robustness of the damage detection routine. The results show that the stochastic modal identification using the stochastic subspace identification (SSI) method had excellent results, thus allowing the location of the damaged region of the structure in all analyzed cases.

<div>This dissertation broadly focuses on autonomous condition assessment of civil infrastructures using vision-based methods, which present a plausible alternative to existing manual techniques. A region-based convolutional neural network (Faster R-CNN) is exploited for the detection of various earthquake-induced damages in reinforced concrete buildings. Four different damage categories are considered such as surface crack, spalling, spalling with exposed rebars, and severely buckled rebars. The performance of the model is evaluated on image data collected from buildings damaged under several past earthquakes taking place in different parts of the world. The proposed algorithm can be integrated with inspection drones or mobile robotic platforms for quick assessment of damaged buildings leading to expeditious planning of retrofit operations, minimization of damage cost, and timely restoration of essential services. </div><div><br></div><div> </div><div> Besides, a computer vision-based approach is presented to track the evolution of a damage over time by analysing historical visual inspection data. Once a defect is detected in a recent inspection data set, its spatial correspondences in the data collected during previous rounds of inspection are identified leveraging popular computer vision-based techniques. A single reconstructed view is then generated for each inspection round by synthesizing the candidate corresponding images. The chronology of damage thus established facilitates time-based quantification and lucid visual interpretation. This study is likely to enhance the efficiency structural inspection by introducing the time dimension into the autonomous condition assessment pipeline.</div><div><br></div><div> </div><div> Additionally, this dissertation incorporates depth fusion into a CNN-based semantic segmentation model. A 3D animation and visual effect software is exploited to generate a synthetic database of spatially aligned RGB and depth image pairs representing various damage categories which are commonly observed in reinforced concrete buildings. A number of encoding techniques are explored for representing the depth data. Besides, various schemes for fusion of RGB and depth data are investigated to identify the best fusion strategy. It was observed that depth fusion enhances the performance of deep learning-based damage segmentation algorithms significantly. Furthermore, strategies are proposed to manufacture depth information from corresponding RGB frame, which eliminates the need of depth sensing at the time of deployment without compromising on segmentation performance. Overall, the scientific research presented in this dissertation will be a stepping stone towards realizing a fully autonomous structural condition assessment pipeline.</div>

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A method that uses machine learning to detect and localize damage in railway bridges under various environmental conditions is proposed and validated in this work. The developed algorithm uses vertical and lateral deck accelerations as damage- sensitive features. Indeed, an Artificial Neural Network (ANN) is trained to predict deck accelerations in undamaged condition given: previous vibration data, air temperature and characteristics of the train crossing the bridge (speed, load position and load magnitude). After an appropriate training period, the comparison between ANN-predicted and measured accelerations allows to compute prediction errors. A Gaussian Process is then used to stochastically characterize prediction errors in undamaged conditions using train speed as independent variable. Recorded vibration data leading to abnormal prediction errors are flagged as damage. The method is validated both on a simple numerical example and on data recorded on a real structure. In the latter case, an appropriate algorithm was developed with the aim of extracting vehicles characteristics from the acceleration time histories. Together with this part of the algorithm for the pre-processing of recorded accelerations, the novelty of the developed method is the addition of air temperature to the input. It allows separating between structure responses that can be flagged as damage from those only affected by environmental conditions.

A damage detection approach based on Artificial Neural Network (ANN), using the statistics of structural dynamic responses as the damage index, is proposed in this study for Structural Health Monitoring (SHM). Based on the sensitivity analysis, the feasibility of using the changes of variances and covariance of dynamic responses of railway bridges under moving trains as the indices for damage detection is evaluated.   A FE Model of a one-span simply supported beam bridge is built, considering both single damage case and multi-damage case. A Back-Propagation Neural Network (BPNN) is designed and trained to simulate the detection process. A series of numerical tests on the FE model with different train properties prove the validity and efficiency of the proposed approach. The results show not only that the trained ANN together with the statistics can correctly estimate the location and severity of damage in the structure, but also that the identification of the damage location is more difficult than that of the damage severity. In summary, it is concluded that the use of statistical property of structural dynamic response as damage index with the Artificial Neural Network as detection tool for damage detection is reliable and effective.

There is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under varying degrees of pressure onboard the orbiter since the beginning of the Space Shuttle Program. After the Columbia accident, the COPVs were re-examined and different studies (e.g. Laser profilometry inspection, NDE utilizing Raman Spectroscopy) have been conducted and can be found in the literature. To explore some of the unique in-house developed hardware and algorithms for monitoring COPVs, this project is carried out with the following general objectives: 1) Investigate the obtaining indices/features related to the performance and/or condition of pressure vessels 2) Explore different sensing technologies and Structural Health Monitoring (SHM) systems 3) Explore different types of data analysis methodologies to detect damage with particular emphasis on statistical analysis, cross-correlation analysis and Auto Regressive model with eXogeneous input (ARX) models 4) Compare differences in various types of pressure vessels First an introduction to theoretical pressure vessels, which are used to compare to actual test specimens, is presented. Next, a background review of the test specimens including their applications and importance is discussed. Subsequently, a review of related SHM applications to this study is presented. The theoretical background of the data analysis methodologies used to detect damage in this study are provided and these methodologies are applied in the laboratory using Composite Overwrapped Pressure Vessels (COPVs) to determine the effectiveness of these techniques. Next another study on the Air Force Research Laboratory (AFRL) Tank that is carried out in collaboration with NASA KSC and NASA MSFC is presented with preliminary results. Finally the results and interpretations of both studies are summarized and discussed.