Análise de falhas em rolamentos por análise de vibração aplicado a aerogeradores

With the heavy use of bearings in various segments of the industry, there are a large number of necessary interruptions in industrial processes to perform maintenance on these devices, with the case study wind turbines. The growth of the wind energy sector, encouraged to conduct research that helps to solve this problem. To contribute to predictive maintenance has been carried out a signal analysis using techniques which allow detection and location of the problem in order to prevent accidents caused and losses due to unexpected equipment failures, whereas low system rotation complicates the detection of the failure. To work around this problem, there was the indication of standard signals for defects in the bearings, making diagnosis of possible failures. With this diagnosis can be performed predictive maintenance, identifying the failure of the system that were tested, such as the introduction of grains of sand in the bearing, wear on the outer race of the bearing and bearing rust. By processing signals it is possible to construct graphs developing a mapping of defects by different peaks in the frequency band.

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.

Sistema para Medição de Velocidades de Escoamentos Através de Ondas Ultra-Sônicas

Está sendo implementado, no Instituto de Engenharia Nuclear-IEN, um projeto de estudos de viabilidade da utilização de técnicas ultra-sônicas para medições de escoamentos bifásicos, ampliando as aplicações do ultra-som na área de pesquisa e na área industrial de medição de fluidos. Na primeira etapa do projeto, foi realizado um estudo preliminar do efeito da velocidade do escoamento em um circuito experimental a água fechado, sobre uma onda ultra-sônica que atravessava o líquido. Foi utilizado um sistema capaz de medir a diferença de tempo de trânsito entre ondas ultra-sônicas da ordem de 1 ns, utilizando técnicas de processamento de sinais. As diferentes velocidades da água obtidas, variando-se a vazão do circuito, foram relacionadas com as velocidades da onda ultra-sônica. Foi observado que a variação da temperatura da água influenciava a velocidade da onda ultra-sônica, de maneira inversa à velocidade do líquido. Pelo experimento realizado, concluiu-se que o dispositivo ultra-sônico montado pode ser adequado para medir a velocidade de escoamento da água no circuito experimental.

Avaliação do comportamento ingestivo utilizando eletromiografia de superfície do músculo masseter

Physiologists and animal scientists try to understand the relationship between ruminants and their environment. The knowledge about feeding behavior of these animals is the key to maximize the production of meat and milk and their derivatives and ensure animal welfare. Within the area called precision farming, one of the goals is to find a model that describes animal nutrition. Existing methods for determining the consumption and ingestive patterns are often time-consuming and imprecise. Therefore, an accurate and less laborious method may be relevant for feeding behaviour recognition. Surface electromyography (sEMG) is able to provide information of muscle activity. Through sEMG of the muscles of mastication, coupled with instrumentation techniques, signal processing and data classification, it is possible to extract the variables of interest that describe chewing activity. This work presents a new method for chewing pattern evaluation, feed intake prediction and for the determination of rumination, food and daily rest time through ruminant animals masseter muscle sEMG signals. Short-term evaluation results are shown and discussed, evidencing employed methods viability.

Arquitetura para o desenvolvimento de unidades de medição fasorial sincronizada no monitoramento a nível de distribuição

This work presents a low cost architecture for development of synchronized phasor measurement units (PMU). The device is intended to be connected in the low voltage grid, which allows the monitoring of transmission and distribution networks. Developments of this project include a complete PMU, with instrumentation module for use in low voltage network, GPS module to provide the sync signal and time stamp for the measures, processing unit with the acquisition system, phasor estimation and formatting data according to the standard and finally, communication module for data transmission. For the development and evaluation of the performance of this PMU, it was developed a set of applications in LabVIEW environment with specific features that let analyze the behavior of the measures and identify the sources of error of the PMU, as well as to apply all the tests proposed by the standard. The first application, useful for the development of instrumentation, consists of a function generator integrated with an oscilloscope, which allows the generation and acquisition of signals synchronously, in addition to the handling of samples. The second and main, is the test platform, with capabality of generating all tests provided by the synchronized phasor measurement standard IEEE C37.118.1, allowing store data or make the analysis of the measurements in real time. Finally, a third application was developed to evaluate the results of the tests and generate calibration curves to adjust the PMU. The results include all the tests proposed by synchrophasors standard and an additional test that evaluates the impact of noise. Moreover, through two prototypes connected to the electrical installation of consumers in same distribution circuit, it was obtained monitoring records that allowed the identification of loads in consumer and power quality analysis, beyond the event detection at the distribution and transmission levels.

Uma contribuição para a medição de distorções harmônicas e inter-harmônicas em instalações de geração fotovoltaica

Dissertação (mestrado)— Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Estudo experimental do escoamento bifásico líquido gás em golfadas com leve mudança de direção

Gas-liquid two-phase flow is very common in industrial applications, especially in the oil and gas, chemical, and nuclear industries. As operating conditions change such as the flow rates of the phases, the pipe diameter and physical properties of the fluids, different configurations called flow patterns take place. In the case of oil production, the most frequent pattern found is slug flow, in which continuous liquid plugs (liquid slugs) and gas-dominated regions (elongated bubbles) alternate. Offshore scenarios where the pipe lies onto the seabed with slight changes of direction are extremely common. With those scenarios and issues in mind, this work presents an experimental study of two-phase gas-liquid slug flows in a duct with a slight change of direction, represented by a horizontal section followed by a downward sloping pipe stretch. The experiments were carried out at NUEM (Núcleo de Escoamentos Multifásicos UTFPR). The flow initiated and developed under controlled conditions and their characteristic parameters were measured with resistive sensors installed at four pipe sections. Two high-speed cameras were also used. With the measured results, it was evaluated the influence of a slight direction change on the slug flow structures and on the transition between slug flow and stratified flow in the downward section.

Arquitetura híbrida com DSP e FPGA para implementação de controladores de filtros ativos de potência

The presence of non-linear loads at a point in the distribution system may deform voltage waveform due to the consumption of non-sinusoidal currents. The use of active power filters allows significant reduction of the harmonic content in the supply current. However, the processing of digital control structures for these filters may require high performance hardware, particularly for reference currents calculation. This work describes the development of hardware structures with high processing capability for application in active power filters. In this sense, it considers an architecture that allows parallel processing using programmable logic devices. The developed structure uses a hybrid model using a DSP and an FPGA. The DSP is used for the acquisition of current and voltage signals, calculation of fundamental current related controllers and PWM generation. The FPGA is used for intensive signal processing, such as the harmonic compensators. In this way, from the experimental analysis, significant reductions of the processing time are achieved when compared to traditional approaches using only DSP. The experimental results validate the designed structure and these results are compared with other ones from architectures reported in the literature.

Método para reconhecimento de vogais e extração de parâmetros acústicos para analises forenses

Forensic speaker comparison exams have complex characteristics, demanding a long time for manual analysis. A method for automatic recognition of vowels, providing feature extraction for acoustic analysis is proposed, aiming to contribute as a support tool in these exams. The proposal is based in formant measurements by LPC (Linear Predictive Coding), selectively by fundamental frequency detection, zero crossing rate, bandwidth and continuity, with the clustering being done by the k-means method. Experiments using samples from three different databases have shown promising results, in which the regions corresponding to five of the Brasilian Portuguese vowels were successfully located, providing visualization of a speaker’s vocal tract behavior, as well as the detection of segments corresponding to target vowels.

A study on independent component analysis over galois fields

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Separação cega de sinais de fala utilizando detectores de voz.

Neste trabalho contemplamos o emprego de detectores de voz como uma etapa de pré- processamento de uma técnica de separação cega de sinais implementada no domínio do tempo, que emprega estatísticas de segunda ordem para a separação de misturas convolutivas e determinadas. Seu algoritmo foi adaptado para realizar a separação tanto em banda cheia quanto em sub-bandas, considerando a presença e a ausência de instantes de silêncio em misturas de sinais de voz. A ideia principal consiste em detectar trechos das misturas que contenham atividade de voz, evitando que o algoritmo de separação seja acionado na ausência de voz, promovendo ganho de desempenho e redução do custo computacional.

Deteção automática de eventos em sinais Doppler de fluxo sanguíneo

Os sinais de fluxo sanguíneo são sinais aleatórios que, além de variarem de individuo para individuo, variam também consoante o ciclo cardíaco em análise. Sendo o diagnóstico de patologias cardiovasculares fortemente baseado em sinais de ultrassom Doppler representados em forma de espectrograma, e tendo em consideração que o ruído do tipo speckle é parte integrante dos sinais ultrassónicos, torna-se vital a pesquisa de métodos de eliminação desse tipo de ruído e de caracterização precisa dos parâmetros desses sinais biomédicos por forma a melhorar a qualidade do diagnóstico clínico. Com esta tese pretende-se desenvolver uma ferramenta computacional que possibilite a extração automática dos parâmetros pico sistólico, fim de diástole e de outros eventos clinicamente relevantes de sinais Doppler de fluxo sanguíneo, com especial atenção ao sinal proveniente da artéria carótida. Esta investigação vem na continuidade de um projeto realizado anteriormente no Grupo de Processamento de Sinal Biomédico da Universidade do Algarve, no qual foi desenvolvido um sistema de redução de ruído de espectrogramas Doppler. Este sistema de remoção de ruído será aqui aplicado e melhorado. Para a deteção e extração automática de parâmetros clínicos, foi desenvolvido um algoritmo que recebe um sinal de Doppler e que tem como saída o espectrograma livre de ruído e os valores dos parâmetros clínicos calculados. O algoritmo desenvolvido está dividido em três partes principais. A primeira, consiste na transformada do sinal para os domínios tempo-frequência para a criação do espectrograma e na aplicação de uma metodologia de remoção do ruído do tipo speckle do espectrograma. A fase seguinte é a criação de um sinal bidimensional a partir do espectrograma, o qual é criado para possibilitar a sua caraterização. Por último, desenvolveu-se uma fase dedicada à caracterização do sinal, tendo como principais funções a deteção dos eventos clínicos de pico sistólico, fim de diástole, índice de pulsação, índice de resistência e ratio sístole-diástole. A refinação de atuação de cada uma das três partes mencionadas pode ser ajustada pelo utilizador, tendo para o efeito sido desenvolvido uma interface gráfica na qual a interação do utilizador com o programa global é facilitada. Versatilidade e eficácia do algoritmo desenvolvido foram demonstradas pelos resultados obtidos com três sinais de Doppler de diferentes origens: um de origem clínica, um sinal de fluxo em artéria carótida simulado experimentalmente recorrendo a phantoms, e um sinal de fluxo simulado computacionalmente. Para cada um destes sinais são apresentados os valores das variáveis considerados preferenciais para o ajuste ao respetivo sinal e os respetivos espetros de ruído reduzido. Os resultados da extração automática dos parâmetros clínicos dos três sinais, comprovou a utilidade clínica do algoritmo desenvolvido.

ALS-Sence: sistema de aquisição e processamento de electromiogramas para pacientes com ALS

Dissertação para obtenção do grau de Mestre em Engenharia de Eletrónica e Computadores