Navegando por Orientadores "MESQUITA, Alexandre Luiz Amarante"

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Acesso aberto (Open Access)
Análise de atributos de classificação para o diagnóstico de falhas em rolamentos baseado em SVM
(Universidade Federal do Pará, 2019-08-06) SOUZA, Jusley da Silva; BAYMA, Rafael Suzuki; http://lattes.cnpq.br/6240525080111166; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
In industries, the concern in total availability of machines and the mechanical equipment in the productive area it’s subject of research and tests to obtain more efficient techniques to be applied for monitoring and faults’ diagnosing. Bearings are machine elements of great application in the industrial area and they present high fault index that generate machine’s stops to carry out maintenance. For this reason, this paper presents Artificial Intelligence technique applied to the vibration signals of a rotary machine for fault diagnosis in its bearings. The vibration signals are part of an open database offered by Case Western Reserve University. In this paper the Support Vector Machine (SVM) classification algorithm is applied in two ways for the rolling bearings faults’ diagnosis. In the first case statistical predictors (Root Mean Square Value, Crest Factor, K Factor, Kurtosis and Skewness) are used as features for the SVM classifier. In the second case, the signal processing is performed by applying the Ensemble Empirical Mode Decomposition (EEMD), which generates several signals called Intrinsic Mode Functions (IMFs). For each IMF, it’s modeled using Autoregressive Modeling (AR), and the AR modeling coefficients of each IMF are used as features for the SVM classifier. The analyzes are performed for training and validation groups, with randomly chosen window and with temporal sequence chosen window, considering two classification problems within the same data, the first one considers the same severity and only changes the fault type and the other vary both severity and fault type. As result, both methodologies presented excellent reliability results for bearing faults’ diagnosis.
Acesso aberto (Open Access)
Análise de sistemas de controle de vibração em máquinas rotativas utilizando atuadores formados por ligas com memória de forma
(Universidade Federal do Pará, 2009-12-04) SILVA, José Adriano Brito da; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245
The application of shape memory alloys (SMA) has been showed as a promising alternative in the vibration control area mainly due to the shape memory and pseudoelastic phenomena which this alloys present. In addition, they show large recovery forces and damping capacity when compared to traditional materials. Despite a great number of papers dealing with SMA abilities applied to vibration control in structures, there are only a few reports about applications of SMA in rotordynamics. Hence, this work focuses basic aspects in the numerical application of SMA actuators for vibration control in rotating machines. In the first analysis of this work it is used a Jeffcott rotor with SMA sleeves placed into one of the bearings. It has been employed different sleeve thickness in the martensite and austenite states and the changes in terms of amplitude and frequency are compared. Furthermore, in the second analysis, two different rotating systems with two discs and SMA springs applied at one and both bearings are analyzed under different set-ups. The springs have been placed externally to bearings and the work temperature is set according to the requirement of vibration control. Moreover, it was used a computational code to represent the thermomechanical behavior of SMA springs as well as a numerical code based on Finite Element Method (FEM) to simulate the dynamic behavior of rotors. The results of the numerical analyses demonstrated the SMA are efficient in the vibration control of rotating systems due to accomplish great reductions in the displacement amplitudes, changes in the critical speeds, suppression of unwanted movements and control of precession orbit shape.
Acesso aberto (Open Access)
Desenvolvimento de sistema de diagnóstico de falhas em roletes de transportadores de correia
(Universidade Federal do Pará, 2024-03-28) SOARES, João Lucas Lobato; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
Belt conveyors are essential equipment in mining industry and require constant monitoring to maintain good reliability. In order to support the belt and the material being conveyed, rollers are components that constantly fail during operation, in which they present faults in bearings and surface wear in the shell as the most common failure modes. Thus, monitoring based on predictive maintenance is essential, and machine learning techniques can be used as an alternative for detecting equipment failures. In diagnostics using machine learning, the feature selection step is important to avoid loss of accuracy in the classification of the equipment's condition. The present study analyzes the performance of the decision tree algorithm and Analysis of Variance (ANOVA) as alternative methods for dimensionality reduction. Initially, the vibration signals were collected on the rollers of a belt conveyor bench and the Wavelet Packet Decomposition (WPD) was applied to the signals to obtain the energy ranges, which were used as features for classification. After the determination of the best features, two approaches were analyzed for the selection of features: one with the application of the method without dimensionality reduction and the other with the application of the decision tree. In addition, different classification algorithms were used: Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN). As a result, it was found a superior performance of diagnostic accuracy in all techniques with a reduction in the dimensionality of the characteristics selected by the decision tree. In addition, SVM, kNN and ANN showed increases in accuracy ranging among the fault diagnosis models approached.
Acesso aberto (Open Access)
Dimensionamento de soluções acústicas para máquina aplicada à extração de fibras do pseudocaule da bananeira
(Universidade Federal do Pará, 2020-02-28) SILVA, Geanilson Brito da; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
The Lake Tucuruí Integration Region (RI) stands out for its banana production in the state of Pará, making it one of the main producers of the fruit. However, the income of farmers in the region with banana production is restricted only to the sale of the fruit, discarding the other parts of the banana tree, which can be used to manufacture products with higher added value. With the use mainly of the fibers of the banana pseudo-stem, several products can be generated, boosting the development of banana cultivation in the region and promoting an increase in employment and income. Aiming at this socioeconomic growth of the RI Lago de Tucuruí, the present work presents, in its first part, an analysis of the fiber extraction machine of the banana pseudo-stem at the Federal University of Pará, Campus de Tucuruí, which will serve the small producers and cooperatives of farmers in the region. In the operation of the machine, it was found that it has noise problems. The sound pressure levels measured around the machine are above the normative limits. Thus, studies were carried out to define acoustic solutions aimed at solving the problem. In the second part, the main focus of this work is to present the acoustic solutions for the fiber extraction machine, including the specification of vibration isolators, replacement of the electric motor fan and dimensioning of the partial enclosure of the machine.
Acesso aberto (Open Access)
Estudo de técnicas de análise modal operacional em sistemas sujeitos a excitações aleatórias com a presença de componente harmônico
(Universidade Federal do Pará, 2006-02-17) CRUZ, Sérgio Luiz Matos da; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245
Traditional modal parameter identification usually require measurements of both the input force and the resulting response in laboratory conditions. However, when modal properties are to be identified from large structures in operation, usually the possibilities to control and measure the loading on the structure is rather limited. In this case, the modal testing is usually performed using response data only. Operational Modal Analysis (OMA) or Operational Modal Testing is a method where no artificial excitation needs to be applied to the structure or force signals to be measured. In this case, the modal parameters estimation is based upon the response signals, thereby minimizing the work of preparation for the test. However, standard OMA techniques, such as NExT, are limited to the case when excitation to the system is a white stationary noise. The NexT assumes that the correlation functions are similar to the impulse response functions, and then, traditional time domain identification methods can be applied. However, if harmonic components are present in addition to the white noise, these components can be misunderstood as natural modes in the plot of response spectrum. In this work, it is shown that it is possible identify if a peak in the response spectrum correspond to a natural mode or an operational mode. It is achieved through the application of the probability density function. It is also presented a modification in the LSCE algorithm in such manner that it can support harmonics in the operational excitation. In order to validate the methods presented in this work, it is shown numerical and experimental cases. In the former, results for a mass-spring-damper of five degree of freedom are presented, and in the latter a beam supporting an unbalanced motor is analyzed.
Acesso aberto (Open Access)
Identificação de danos em estruturas usando modelo preditor baseado em técnicas de aprendizagem de máquinas
(Universidade Federal do Pará, 2019-10-04) BONA, Vanessa Cordeiro de; BAYMA, Rafael Suzuki; http://lattes.cnpq.br/6240525080111166; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
The increase in the number of new buildings and the existence of countless old buildings, whether small or large, call attention to the need for measures that maintain the quality, safety and useful life of the structures. Inspections and monitoring, regardless of the age of the building, are essential to detect the existence of damage, especially in its initial phase, avoiding its propagation or serious consequences that originate due to a collapse of the structure, due to the high degree deterioration and no recovery techniques. Based on these aspects, this dissertation has the general objective of detecting damage in structures using the machine learning approach, which integrates three techniques: initially the Ensemble Empirical Mode Decomposition (EEMD) is applied a processing of the signals and seeks to adapt them for the application of the Auto Regressive Model (AR) generating the attributes, which will serve as input patterns for the Support Vector Machine (SVM) classifier. The data used to apply the methods come from the modeling of bi-supported steel beams, intact and with damaged regions, by the SAP 2000 Structural Analysis Software. With reference to the creation of the structures by finite elements, two types of loads were applied . The first case of random loading acting in only one point of the beam and the second case with three simultaneous loads in three points of the beam. According to variations in the location and degree of severity of the damage, the study sought to assess the ability of the predictive models to classify the data correctly. In the analyzes with greater mass losses, the accuracy values are higher, decreasing according to the reduction of the damage geometry, as the signs of displacement become similar to the integral structure. Regarding the number of loads, the method demonstrated better performance and accuracy in cases with three simultaneous loads.
Acesso aberto (Open Access)
Proposta de metodologia para diagnóstico de falha em rolamentos de baixa velocidade
(Universidade Federal do Pará, 2024-04-19) COSTA, Thiago Barroso; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245; https://orcid.org/0000-0001-5605-8381
Monitoring low-speed bearings with vibration analysis is more challenging due to the low energy level of the vibration signal that carries the failure data, making it more susceptible to interference from other sources, impairing the interpretation of information. Thus, an alternative is to calculate signal predictors that may be sensitive to pattern changes relative to failure onset and progression. Hence, the present work extracted different types of features, among them two nonlinear features and eleven extracted from the signal in the time domain. Those features were ranked and selected based on their sensibility to class differentiation, which was estimated using the t-Welch statistic value. Among them is the Largest Lyapunov Exponent, which, in this work, had a modification in one of its calculation steps, improving its sensitivity in some cases. In addition, the influence of the vibration signal window size on the class separability of the indicators was evaluated (which is a scarce content in low-speed bearing monitoring literature). After feature selection, the data were subjected to a linear transformation through PCA (Principal Component Analysis), aiming to reduce the data dimensionality to three dimensions and to minimize the redundancy effects of highly correlated features. In sequence, the data represented in the space of principal components were projected on a Hotelling T2 statistic control chart. The chart allowed the detection and rejection of potential outliers, which consisted of points above a limit line estimated based on F statistic distribution. Finally, binary and multiclass Support Vector Machine classification models were trained with experimental data acquired from normal conditions and three levels of incipient fault in bearing. The models performed well, mainly the binary model with test data obtained from belt conveyor pulley bearings in industrial operation.