PAULO HENRIQUE FERRAZ MASOTTI

PAULO HENRIQUE FERRAZ MASOTTI

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Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)
2020 - SERRA, PEDRO L.S.; MASOTTI, PAULO H.F.; ROCHA, MARCELO S.; ANDRADE, DELVONEI A. de; TORRES, WALMIR M.; MESQUITA, ROBERTO N. de
The International Atomic Energy Agency (IAEA) has been encouraging the use of passive cooling systems in new designs of nuclear power plants. Next nuclear reactor generations are intended to have simpler and robust safety resources. Natural Circulation based systems hold an undoubtedly prominent position among these. The study of limiting conditions of these systems has led to instability behavior analysis where many different two-phase flow patterns are present. Void fraction is a key parameter in thermal transfer analysis of these flow instability conditions. This work presents a new method to estimate void fraction from images captured of an experimental two-phase flow circuit. The method integrates a set of Artificial Neural Networks with a modified Randomized Hough Transform to make multiple scans over acquired images, using crescent-sized masks. This method was called Randomized Hough Transform with Neural Network (RHTN). Each different mask size is chosen according with bubble sizes, which are the main ‘objects of interest’ in this image analysis. Images are segmented using fuzzy inference with different parameters adjusted based on acquisition focus. Void fraction calculation considers the volume of the imaged geometrical section of flow inside cylindrical glass tubes considering the acquisition depth-of-field used. The bubble volume is estimated based on geometrical parameters inferred for each detected bubble. The image database is obtained from experiments performed on a vertical two-phase flow circuit made of cylindrical glass where flow-patterns visualization is possible. The results have shown that the estimation method had good agreement with increasing void fraction experimental values. RHTN has been very efficient as bubble detector with very low ‘false-positive’ cases (< 0.004%) due robustness obtained through integration between Artificial Neural Networks with Randomized Hough Transforms.
Two-phase flow bubble detection method applied to natural circulation system using fuzzy image processing
2018 - BUENO, R.C.; MASOTTI, P.H.F.; JUSTO, J.F.; ANDRADE, D.A.; ROCHA, M.S.; TORRES, W.M.; MESQUITA, R.N. de
Natural circulation cooling systems are currently used in new nuclear reactors. Over the last decades, research in these systems has focused in the study of flow and heat transfer parameters. A particular area of interest is the estimation of two-phase flow parameters by image processing and pattern recognition using intelligent processing. Several methods have been proposed to identify objects of interest in bubbly two-phase images. Edge detection is an important task to estimate flow parameters, in which the bubbles are segmented to obtain several features, such as void fraction, area, and diameter. However, current methods face difficulties in determining those parameters in high bubble-density two-phase flow images. Here, a new edge detection method is proposed to segment bubbles in natural circulation instability images. The new method (Fuzzy Contrast Standard Deviation – FUZCON) uses Fuzzy Logic and image standard deviation estimates of locally measured contrast levels. Images were obtained through an experimental circuit made of glass, which enables imaging flow patterns of natural circulation cycles at ambient pressure. The results indicated important improvements on edge detection efficiency for high void fraction estimation on high-density two-phase flow bubble images, when compared to classical detectors, without the need to use smoothing algorithms or human intervention.
The Paraconsistent Fuzzy Logic using wavelet zero crossings for the diagnostic of defects in rolling bearings
2003 - TING, D.K.S.; MASOTTI, P.H.F.
Wavelet Zero Crossings and Paraconsistent Fuzzy Logic in the diagnostic of rolling bearings
2002 - MASOTTI, P.H.F.; TING, D.K.S.
The Paraconsistent Fuzzy Logic using wavelet zero crossings for the diagnostic of defects in rolling bearings
2003 - TING, D.K.S.; MASOTTI, P.H.F.
Automatic diagnosis of defects in bearings using Fuzzy Logic
2001 - VICENTE, S.A.S.; MASOTTI, P.H.F.; ALMEIDA, R.G.T.; TING, D.K.S.; PADOVESE, L.R.
In order to improve industrial competitiveness, the cost reduction in industrial plant maintenance is becoming increasingly important. A methodology applied to improve reliability in the production and to reduce operational costs is based on predictive maintenance. In this context there is a need for the optimization of diagnosis systems in order to increase precision and to reduce human errors. The automation of diagnosis processes results directly in improved reliability for decision taking. The problem caused by errors in the diagnosis tends to be amplified when large an industrial plant is concerned where a large number of monitored points is needed. Automatic diagnosis systems should be robust to a point where it must operate with a diversified source of information allowing for analysis of different equipment and existing defects and problems. The Fuzzy Logic is applied in the present work since as it is well known, this technique is a flexible tool, which allows the modeling of uncertain, and ambiguous data frequently found in real situations. In the classical logic theory, a element is either part of a given set or not. In fuzzy logic theory, a element can be or not part of a given set and also, can partially be a member of the set thus characterizing the fuzzy sets. This work presents an automatic diagnosis system for the classification of defects in bearings based on fuzzy logic. The system was developed to classify different types of defects in rolling bearings operating under several rotating speeds and load conditions. The experimental data bank used was obtained in a bearing defects simulator apparatus. The acquired signals were analyzed by statistical and spectral techniques for monitoring and diagnosis such as RMS, skewness, kurtosis, and power spectrum density methods The results obtained by using fuzzy logic for classification were conclusive showing that the system is capable to identify and to classify defective bearings.
Teste de validade para um ensaio por Eddy Current em tubos de geradores de vapor usando logica 'fuzzy' paraconsistente
2000 - TING, D.K.S.; MASOTTI, P.H.F.; MESQUITA, R.N.
Metodologia de monitoração e diagnóstico automatizado de rolamentos utilizando lógica paraconsistente, transformada de Wavelet e processamento de sinais digitais
2006 - MASOTTI, PAULO H.F.
A área de monitoração e diagnóstico vem apresentando um acentuado desenvolvimento nos últimos anos com a introdução de novas técnicas de diagnóstico bem como vem contando com a contribuição dos computadores no processamento das informações e das técnicas de diagnósticos. A contribuição da inteligência artificial na automatização do diagnóstico de defeito vem se desenvolvendo continuamente e a crescente automação na indústria vêm de encontro a estas novas técnicas. Na área nuclear, é crescente a preocupação com a segurança nas instalações, e têm sido procuradas técnicas mais eficazes para aumentar o nível de segurança [59]. Algumas usinas nucleares já possuem instaladas, em algumas máquinas, sensores que permitem a verificação de suas condições operacionais. Desta forma, este trabalho também pode colaborar nesta área, ajudando no diagnóstico das condições de operação das máquinas, mais especificamente, no diagnóstico das condições dos rolamentos. O principal objetivo deste trabalho é detectar e classificar os tipos de defeitos apresentados pelos rolamentos analisados e para tal desenvolveu-se uma nova técnica de extração de característica dos sinais de aceleração, baseando-se no Zero Crossing da Transformada de Wavelet contribuindo com o desenvolvimento desta dinâmica área. Como técnica de inteligência artificial foi utilizada a Lógica Paraconsistente Anotada com dois valores (LPA2v), oferecendo a sua contribuição na automação do diagnóstico de defeitos, pois esta lógica pode tratar inclusive de resultados contraditórios que as técnicas de extração de características possam apresentar. Foi desenvolvido um programa de computador onde varias técnicas de extração de características foram utilizadas para realização de diagnóstico das condições de operação dos rolamentos. Este programa foi testado através de dados experimentais obtidas em uma bancada de ensaios para rolamentos onde defeitos previamente conhecidos foram utilizados para avaliar o desempenho das novas técnicas utilizadas. Este trabalho também se concentrou na identificação de defeitos em sua fase inicial procurando utilizar acelerômetros, pois são sensores robustos, de baixo custo e facilmente encontrados na indústria em geral. Os resultados deste trabalho foram obtidos através da utilização de um banco de dados experimental e verificou-se que os resultados de diagnósticos de defeitos mostraramse bons para defeitos em fase inicial.
Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis
2012 - MESQUITA, R.N. de; MASOTTI, P.H.F.; PENHA, R.M.L.; ANDRADE, D.A.; SABUNDJIAN, G.; TORRES, W.M.; MACEDO, L.A.