PPG.EGPSA/ITEGAM
URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1
A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.
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34 resultados
Resultados da Pesquisa
Item Proposta de um algoritmo de detecção de falhas com lógica fuzzy para otimização de motores elétricos em processos de fundição de alumínio(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SILVA, Lenildo Marcos da Mota; BRITO JUNIOR, Jorge de AlmeidaThis study proposes the development of a fault detection system based on fuzzy logic to optimize electric motors in aluminum casting processes. The theme is grounded in the need to enhance efficiency and operational continuity in the casting industry, minimizing unplanned downtime and maintenance costs. The primary objective is to create a fuzzy control model that enables real-time monitoring of critical variables, such as temperature, pressure, and electric current, to facilitate predictive fault detection. Methodologically, operational scenario simulations were conducted to evaluate the model’s performance under adverse conditions, including overload and overheating, with validation through experimental testing. Results indicate that the proposed fuzzy system accurately identifies anomalies and issues preventive alerts, extending motor lifespan and reducing downtime. It is concluded that the model can be integrated with supervisory systems like SCADA, enhancing predictive maintenance and operational efficiency. This technological solution shows promise for the aluminum casting industry and potential applications in other industrial sectors.Item Hardware didático para mapeamento indoor com LiDAR: uma abordagem para a Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) GUABIRABA, Rafael Braga; MARTINELLI FILHO, NelsonThe Manaus Industrial Hub is an innovation center driven by industry, government, and education, requiring accelerated workforce qualification for Industry 4.0. The rapid technological transition demands effective training strategies, reducing dependence on external models. Therefore, the development of accessible educational systems is essential to prepare professionals for this new reality. This dissertation proposes a compact architecture capable of supporting ROS, focusing on indoor environment mapping. This platform is designed for educational purposes, providing an introductory experience in robotics. The selected materials prioritize portability, utilizing a Raspberry Pi 4 Model B and an RPLidar 360° laser sensor. The results demonstrate the system's feasibility for both educational and industrial applications, contributing to the training of skilled professionals for Industry 4.0.Item Optimization of Economic and Environmental Dispatch Using Bio-inspired Computer Metaheuristics(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) NASCIMENTO, Manoel Henrique Reis; CAMPOS, Paola SoutoThis chapter addresses the optimization of economic and environmental dispatch in electric power systems using bio-inspired computational metaheuristics. Techniques such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony-Based Algorithms are analyzed. The study demonstrates how these approaches can reduce operational costs and minimize environmental impacts, ensuring energy efficiency and sustainability.Item Desenvolvimento de um Sistema de Monitoramento de Carga em Baterias Fotovoltaicas Utilizando Python(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) David Barbosa de; LEITE, Jandecy CabralThe article presents a Load Monitoring System for Photovoltaic Batteries developed in Python, aiming to provide users with precise and real-time information about the charge status of the batteries. This system is particularly relevant due to the increasing adoption of photovoltaic solar energy as a renewable energy source, allowing the optimization of photovoltaic systems' performance and contributing to more efficient energy consumption management.Item Avaliação de imunidade eletromagnética radiada do protocolo IOT LORA(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) RIBEIRO, Jhonatan; SANTOS, Eliton Smith dosIndustrial automation, historically based on wired technologies, has evolved toward wireless solutions, driven by the increasing interconnection of devices and the need to overcome logistical and range limitations. In Industry 4.0, wireless communication plays a central role by integrating sensors, actuators, and control systems into an intelligent production environment. This integration enables real-time data collection and analysis, facilitating automation, optimization, and process flexibility. Despite the benefits, adopting wireless technologies faces significant challenges, particularly electromagnetic interference (EMI), which can compromise the operation of devices and systems. This study presented an analysis of the electromagnetic immunity of the LoRa protocol, using the Heltec V2 board, which incorporates the SX1278 chip, to evaluate its robustness in environments subject to electromagnetic interference. The tests, conducted according to the IEC 61000-4-3 standard, identified critical failure frequencies such as 232.4 MHz, 412 MHz, and 615.2 MHz under different parameter configurations. In these conditions, partial or total communication loss was observed, depending on the intensity of the irradiated field, demonstrating that different configurations can significantly impact the system's immunity. Solutions such as using robust protocols, including LoRaWAN and NB-IoT, spectral spreading techniques, electromagnetic shielding, and filters can mitigate the effects of EMI. Furthermore, future strategies include developing adaptive algorithms to dynamically adjust communication parameters and signal failures, enhancing the resilience and reliability of the LoRa protocol in complex industrial environments. The results of this research contribute to improving wireless communications in Industry 4.0, highlighting LoRa as an essential technology for robust and efficient connectivity.Item Desenvolvimento de um dispositivo portátil para monitoramento de subestações elétricas integrado a plataforma SGE: uma aplicação na indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SANTOS JUNIOR, Hélio Andrade dos; ALENCAR, David Barbosa deThe dissertation focuses on the development of a portable device for monitoring electrical substations, integrated with the SGE platform, utilizing Industry 4.0 technologies like the Internet of Things (IoT) and real-time data analysis. The primary objective was to create a solution to identify and mitigate energy losses, optimize consumption, and reduce operational costs. To achieve this, the research employed an inference model based on fuzzy logic and developed a real-time process control (RTPC) framework. The device and its monitoring software were validated through field tests in real substations, showing positive results in reducing losses and improving energy efficiency. This work contributes to the modernization of electrical substation management, aligning with sustainability trends and technological advancements in the energy sector.Item Utilização de Inteligência Artificial e IoT para Inspeção e Detecção de Defeitos em Placas de Circuito Impresso(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOARES, Karen Kettelen Souza; GUIMARÃES, Gil EduardoThe increasing complexity in the manufacturing of printed circuit boards (PCBs) has posed significant challenges for industries, particularly in the inspection and detection of defects. This study proposes the use of Artificial Intelligence (AI) and the Internet of Things (IoT) as tools to optimize fault detection in PCBs, focusing on reducing human errors and improving operational efficiency. The research is based on the implementation of an automated inspection system, utilizing neural networks for defect classification and IoT sensors for real-time monitoring of manufacturing conditions. The main goal of the dissertation is to develop an efficient analysis model to identify early faults, enhance product quality, and reduce operational costs. The study involved building a functional prototype that integrates AI and IoT, validated in a real production environment. The results showed that the proposed approach could detect defects with higher accuracy, leading to significant improvements in the efficiency of the PCB production line.Item Arquitetura computacional para controle de monitoramento de qualidade de energia utilizando ferramentas de inteligência artificial focado na Indústria 4.0 com integração de energias renováveis(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) KAMIO, Edson; GUIMARÃES, Gil EduardoElectric power quality (QEE) is crucial for the efficiency of industrial systems in Industry 4.0. This dissertation proposes an innovative computational architecture to control and monitor QEE, integrating renewable energy sources and artificial intelligence (AI). Key metrics like power factor (PF) and total harmonic distortion (THD) directly influence operational costs and equipment lifespan. AI-based predictive strategies dynamically manage capacitor banks and harmonic filters, mitigating issues such as inadequate PF and high THD. Reactive power compensation (CPR) is central to improving PF, reducing system losses, and enhancing energy efficiency. The system also integrates solar energy management, optimizing energy savings and sustainability. Data is collected using IMS Smart Cap 485 devices via the Modbus-RTU protocol, measuring voltage, current, active/reactive power, and THD. Stored in a MySQL database, the data is analyzed in real time with deep learning (LSTM) and optimization (AG) algorithms. Python-based dashboards visualize data, predict network issues, and support strategic actions, such as activating capacitors and THD filters. This approach highlights AI's role in energy efficiency and reducing reliance on conventional sources. Gaps in literature include the lack of interoperability standards, the need for explainable AI algorithms, and limited longitudinal studies on real-world applications. This research addresses these challenges, contributing to Industry 4.0's sustainability and efficiency goals.Item Development of Intelligent Devices for Communication and Data Pre-Processing in the Process of Electronic Meter Testing(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) RAMOS JUNIOR, Juarez da Silva; LEITE, Jandecy Cabral; GOMES, Marivan Silva; PAULA, Railma Lima de; CARVALHO, Michael da Silva; SILVA, Ítalo Rodrigo Soares; SIQUEIRA JUNIOR, Paulo Oliveira; PARENTE, Ricardo Silva; MIRANDA, Luís Gabryel dos Santos; LEITE, Jandecy CabralThis study investigates the development of intelligent devices for communication and data pre-processing in electronic meter testing. The research addresses the lack of efficient communication between electronic devices and the need for information synchronization to support decision-making in Industry 4.0. The article presents the development of a Lean Manufacturing Intelligent System, integrating API and machine learning algorithms for demand forecasting and process optimization. The results include embedded firmware for data collection, forecasting algorithms, and an interactive dashboard for production monitoring. The proposed solution aims to reduce costs and improve product quality.Item Algoritmo de Detecção de Falhas com Lógica Fuzzy para Otimização de Motores Elétricos (ADFLFOM)(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) NASCIMENTO, Manoel Henrique Reis; LEITE, Jandecy CabralThis document certifies the registration of the software Fault Detection Algorithm with Fuzzy Logic for Electric Motor Optimization (ADFLFOM), developed in the Python programming language. The system applies fuzzy logic to detect and optimize the operation of electric motors, identifying potential failures and suggesting operational adjustments for greater energy efficiency. The software was registered with the National Institute of Industrial Property (INPI) under number BR512025000360-9, valid for 50 years from January 1st following the date of 12/02/2024.