Doutorado em Engenharia Elétrica
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- ItemFBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity(Universidade Federal do Espírito Santo, 2024-06-03) Lazaro, Renan Costa; Frizera Neto, Anselmo; https://orcid.org/0000-0002-0687-3967; http://lattes.cnpq.br/; Leal Junior, Arnaldo Gomes; https://orcid.org/0000-0002-9075-0619; http://lattes.cnpq.br/; Orientador2; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; Pontes, Maria José; https://orcid.org/0000-0002-9009-2425; http://lattes.cnpq.br/; Marques, Carlos Alberto Ferreira; https://orcid.org/; http://lattes.cnpq.br/; Theodosiou, Antreas; https://orcid.org/; http://lattes.cnpq.br/This doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The research introduces FBG-based for tank structural health monitoring and parameters measurements in fluids, such as temperature, level, thermal conductivity and salinity. Experimental results demonstrate the efficacy of these sensors in challenging industrial conditions. The thermal experiments, utilizing an FBG-based temperature sensor, reveal insights into thermal power distribution in liquid processing systems. Specific heat and thermal conductivity of water are successfully estimated, demonstrating increased thermal stability with higher heat power. A method for measuring heat transfer rate in liquids is proposed, showing potential applications in industrial contexts. In the realm of structural health monitoring (SHM), the quasi-distributed FBG sensor, combined with supervised machine learning, exhibits high accuracy in monitoring stress and deformation in oil tank structures. The Random Forest algorithm enables precise liquid level estimation with minimal error, contributing to predictive maintenance strategies. The development of an all-optical hot-wire sensor showcases its precision in assessing thermal conductivity and salinity in various fluids. The sensor, integrating FBG with a hot-wire component, proves effective in discriminating substances with close thermal conductivity values. Future work aims at reducing measurement times and adapting the sensor for direct salinity measurement. Finally, worth highlighting the significant contributions of each sensor, emphasizing the practical applicability and promising results obtained in thermal analysis, structural health monitoring and all-optical sensing for the oil and gas industry. The research sets the stage for further exploration and refinement of these sensor technologies in complex industrial scenarios.
- ItemMethodologies to improve one-class classifier performance applied to multivariate time series(Universidade Federal do Espírito Santo, 2024-04-05) Machado, André Paulo Ferreira; Co-orientador1; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador2; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador3; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador4; ID do co-orientador4; Lattes do co-orientador4; Orientador1; https://orcid.org/; http://lattes.cnpq.br/; Orientador2; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; 1º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 2º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 3º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 4º membro da banca; http://lattes.cnpq.br/; 5º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 6º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 7º membro da banca; https://orcid.org/; http://lattes.cnpq.br/This work proposes novel methodologies to improve the performance of one-class classifiers applied to multivariate time series data. The main method is through clustering of multivariate time series. Datasets arising from real processes come from the available sensors and are affected by many factors, such as aging of the process, changes in the operation region, and equipment malfunction. Despite that, one expects that the classes represented by such diverse data can be unveiled via trained classifiers. This work hypothesizes that the overall performance can be improved by training sets of one-class classifiers with subsets of data clustered by similarity, obtained by DTW Barycenter Averaging (DBA) which is used to measure the similarity between the time series and each cluster. The proposed method is applied to one class classifiers since they are trained only with the target class, which is clustered based on time series similarity using Dynamic Time Warping and k-means. Additionally, a second approach is proposed, called time-shift of labels, to improve the differentiation between normal and faulty data. This method is applied during the training phase and focuses on particular situations involving the transition from normality to faulty data, where the boundaries are difficult to differentiate (overlapping data). The time-shift results show a mitigation of the effect of overlapping data. The advantages of the techniques are illustrated through their application to two public datasets one from the oil industry with instances characterizing eight classes of data represented by five time series (3W dataset), and another from a hydraulic system for the study of typical hydraulic system failures with five classes and seventeen time series (Condition monitoring of hydraulic systems - ICM dataset). For the 3W dataset, seven classes are selected to train Long Short Term Memory (LSTM) classifiers using the variables and instances clustered using time series clustering algorithms. The results demonstrate that increasing the similarity of training data tends to improve the performance of the LSTM classifier, achieving an increase of 10% in the overall performance on the 3W dataset. In a specific case, where the clustering model raised the similarity by 84%, the classification performance improved by 21%. For condition monitoring of hydraulic system data, the proposed method achieved a significant performance improvement of over 40% compared to the baseline model. Notably, in the specific case of leakage fault, the classification performance improvement rises by 64%
- ItemDetecção de eventos de obstrução em válvula no processo de lingotamento contínuo com uso de aprendizado de máquinas(Universidade Federal do Espírito Santo, 2024-05-25) Diniz, Ana Paula Miranda; Co-orientador1; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador2; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador3; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador4; ID do co-orientador4; Lattes do co-orientador4; Orientador1; https://orcid.org/; http://lattes.cnpq.br/; Orientador2; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; 1º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 2º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 3º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 4º membro da banca; http://lattes.cnpq.br/; 5º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 6º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 7º membro da banca; https://orcid.org/; http://lattes.cnpq.br/The continuous casting process, used in the manufacture of steel plates, is currently the most economical and efficient way of production within the industry. Although continuous casting is a widely used process, some problems associated with the process have not yet been resolved, one of them being the obstruction of the Submerged Entry Nozzle (SEN), which controls the flow of steel between the tundish and the mold. This obstruction, also called clogging, not only impairs the quality of the product but also results in lower process yield, resulting in losses. Thus, clogging detection is of fundamental importance, because control actions can allow the system to operate for a longer time. In this work, methodologies based on Machine Learning and Deep Learning are presented and compared to detect the occurrences of clogging from historical data of process variables. In general, the performance of the classifiers achieved very promising results in real data with unbalanced classes. In particular, the method employing spatiotemporal analysis, using four process variables, obtained a remarkably superior performance when compared to the others, reaching averages of Precision and Recall, respectively, of 95.53% and 97.33%. To reduce the false positive and negative rates, a post-processing heuristic was implemented and applied to the model output, achieving a Precision and a Recall, respectively, of approximately 98% and 99%. To the best of our knowledge, these results have never been found in the literature. Although a detailed comparison is unfeasible due to the differences between the datasets and their inaccessibility, the modeling proposed here reached higher performance levels when compared to the results found in the literature to solve this industry’s problem. The high and unprecedented results obtained in this work, therefore, will contribute both to the improvement of the quality of the final product and to the reduction of costs associated with steel production, since the more effective classification of clogging occurrences can help operators in the corrective action planning
- ItemDevelopment of a Mixed Reality Environment for the Rehabilitation of People with Impaired Mobility Using Gait Support Strategies(Universidade Federal do Espírito Santo, 2024-04-22) Machado, Fabiana Santos Vieira; Díaz, Camilo Arturo Rodríguez; https://orcid.org/0000-0001-9657-5076; http://lattes.cnpq.br/2410092083336272; Frizera Neto, Anselmo; https://orcid.org/0000-0002-0687-3967; http://lattes.cnpq.br/8928890008799265; https://orcid.org/0000-0003-0996-8651; http://lattes.cnpq.br/2705690076290294; Lima, Eduardo Rocon de; https://orcid.org/0000-0001-9618-2176; http://lattes.cnpq.br/6623746131086816; Alsina, Pablo Javier; https://orcid.org/0000-0002-2882-5237; http://lattes.cnpq.br/3653597363789712; Hernández, Mario Fernando Jiménez; https://orcid.org/0000-0003-0965-277X; http://lattes.cnpq.br/6078067029625341; Mello, Ricardo Carminati de; https://orcid.org/0000-0003-0420-4273; http://lattes.cnpq.br/1569638571582691Mobility significantly impacts quality of life, yet various health conditions can hinder it. These challenges are not limited to disease or injury-related impairments but also extend to age-related physical, cognitive, and sensory function losses. Smart Walkers and rehabilitation robotics offer solutions to improve functional capabilities and tailor therapy to individual needs, and in conjunction with Mixed Reality (MR), can also enhance motivation. This Doctoral Thesis aims to integrate advanced human-robot interfaces withMixed Reality to develop effective rehabilitation strategies. The UFES vWalker, a novel robotic assistance device introduced in this thesis, utilizes sensor interfaces to translate users' movement intentions into safe navigation. Integrated into an MR system, it combines virtual and physical environments and sensors, offering multimodal sensory feedback and enhanced human-robotenvironment interaction. The initial experiment with the MR system integrated haptic and visual feedback. Haptic feedback simulated an impedance tunnel to aid movement along the path, while visual feedback displayed the path in the virtual environment. Users with visual feedback completed tasks faster than those with only haptic feedback. The following experiment introduced a multimodal feedback system to assist visually impaired individuals in navigation. It included two main feedback systems: audio cues to guide users and vibration alerts for virtual obstacles. To prevent volunteers from viewing the virtual environment, visual feedback from the Oculus Quest headset, was disabled, creating a virtual blindfold. Three control strategies were used, each one designed for people with different residual mobility and cognitive capabilities. The strategies that offer more and less autonomy were more successful among volunteers, and exhibited similar mental, and physical demand. In the last experiment, a virtual obstacle avoidance strategy was introduced, utilizing a virtual laser sensor. This approach allowed users to move freely until an obstacle was detected, upon which the controller assists in navigating around it. Moreover, an interface was created to offer visual feedback on the key elements of the developed strategy. The volunteers found the MR system enjoyable, realistic, and encountered minimal confusion or difficulty during the experiment. Also, volunteers who received no introductory explanation about the interface were mostly able to infer their purpose. Therefore, it is clear that MR systems can provide considerable benefits to users who use rehabilitation assistance devices.
- ItemMONITORAMENTO DE SINAIS CRONOBIOLÓGICOS EM AMBIENTES ISOLADOS(Universidade Federal do Espírito Santo, 2023-05-31) Marins, Daniela Pawelski Amaro; Segatto, Marcelo Eduardo Vieira; https://orcid.org/000000034083992X; http://lattes.cnpq.br/2379169013108798; https://orcid.org/0000-0002-1315-1313; http://lattes.cnpq.br/3872630194188262; Alvarez, Cristina Engel de; https://orcid.org/0000000238988515; http://lattes.cnpq.br/5240388600131197; Pontes, Maria Jose; https://orcid.org/0000000290092425; http://lattes.cnpq.br/4148956242627659; Fardin, Jussara Farias; https://orcid.org/000000034785556X; http://lattes.cnpq.br/1912113095988528; Diaz, Camilo Arturo Rodriguez; https://orcid.org/0000000196575076; http://lattes.cnpq.br/2410092083336272; Co, Marcio AlmeidaLight plays an important role in the architecture and well-being of users of built spaces, as its intensity and spectral composition influence the circadian rhythm of the human body. The human body is a physiological system that regulates its sleep-awake cycle through a constant rhythm of light and darkness, divided into 24-hour periods, delimited by the dynamics of our own planet. For a long time, the research field has been concerned with understanding this rhythm to improve people's quality of life. In order to provide a better understanding of the influence of light on the human circadian rhythm, a remote monitoring device was developed that reliably measures the light spectrum and human circadian rhythm in different environments, including Antarctica as the main case study and a comparative cross-sectional urban and tropical study. The proposed equipment was developed to help understand how light influences the human circadian rhythm and make these measurements accessible with low-cost tools. The results showed that the developed monitoring prototype is capable of collecting and transmitting environmental and human data reliably. The cross-sectional analysis of the collected data revealed evidence of the significant influence of light on the regulation of the human circadian rhythm in urban environments and in Antarctica. Understanding the influence of light on the human circadian rhythm can have important implications for human health and well-being. Therefore, the low-cost equipment developed can be reproduced and used by research institutions to collect data in different environments and improve the understanding of the influence of the light spectrum on the human circadian rhythm.