TCC - Automação Industrial (UACSA)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2918
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Resultados da Pesquisa
Item Sistema de supervisão e controle da planta piloto do LIAC(2020-11-04) Silva, Natasha Milena do Santos da; Cervantes, Ania Lussón; Gomes, Flávio da Silva Vitorino; http://lattes.cnpq.br/3701230748191590; http://lattes.cnpq.br/5793691069517813Industrial automation is an area that stands out more and more for the benefits it brings to the production process, they are: improving quality and productivity and reducing costs. Therefore, it is extremely important that engineering graduates put their knowledge on the subject into practice, and didactic plants are great tools for this purpose. This article presents a supervision and automatic control system of the pilot plant present in the Laboratory of Instrumentation, Automation and Control (LIAC) at UACSA-UFRPE, aiming to cover the first three levels of the automation pyramid: field devices, control and supervision. The system architecture consists of two reservoirs, a programmable logic controller S7-1200, sensors, actuators, and the Elipse SCADA and Matlab software. The methodology for modeling using experimental identification for the level system is presented applying an input step to the plant, with the data being accessed from the supervisory system by the network through the OPC protocol. With the model obtained, simulation and experimentation results are presented using the Proportional-Integral (PI) control strategy for changes in the reference. Finally, a plant supervision and control system and a human-user interface are developed, allowing supervision of the four process variables: level, temperature, pressure and flow, and the level control in the upper reservoir. In addition, it is possible to manage alarms, historical records and reports, for decision making in failure prevision, maintenance and troubleshooting.Item Avaliação de algoritmo de clusterização para segmentação de nuvens de pontos 3D(2022-10-05) Melo, Davi de Almeida; Ferreira, Felipe Alberto Barbosa Simão; http://lattes.cnpq.br/9939255113143786; http://lattes.cnpq.br/35285523939516023D technologies have been explored in many industrial fields. With those technologies functions such as visualization, instrumentation, control, simulation, training, planning, documentation, and more are performed. From that, new types of media were introduced to the industrial context. For instance, the point clouds, which is a set of points distributed on a tridimensional model of reality. They are often built through the operation of a scanner and may contain the characteristics of an object, such as localization, color, reflectance and more in every one of its points. Given that the point clouds represent pieces, equipments, piping, machines, areas and structures in the industrial context, a useful tool is to segment the point clouds and enable a better visualization of separate parts of the same structure. Furthermore, among the presented 3D technologies, point cloud segmentation permeates directly and indirectly the fields of visualization, instrumentation, and control. Thus, verified the importance of the point cloud segmentation, the goal of this monography is to assess two clustering algorithms for 3D point cloud segmentation. They are the DBSCAN and the K-means. Both are categorized as clustering algorithms through unsupervised machine learning. After the assessment, the differences between each algorithm were found. The K-means was verified to have better performance when it comes to disperse data and the equivalent to the DBSCAN when it comes to data distribution with a certain distance between the clusters.