Navegando por Autor "SILVA, Ítalo Rodrigo Soares"

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Algoritmo de Previsão da Confiabilidade de Motores (APCM)
(Instituto Nacional da Propriedade Industrial (INPI), 2023-05-09) SILVA, Ítalo Rodrigo Soares; NASCIMENTO, Manoel Henrique Reis; http://lattes.cnpq.br/0850846128967798
Registration of computer program registered with the National Institute of Industrial Property (INPI), work derived from the dissertation "KPI reliability prediction model in a group of internal combustion machines using artificial neural network techniques in thermoelectric plants".
Análise decisória para implementação de estratégias integradas para a redução de setup em processos industriais utilizando lógica fuzzy
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-06-09) SOUZA, Vanessa Silva; ALENCAR, David Barbosa de; SILVA, Ítalo Rodrigo Soares
This article explores the application of Fuzzy Logic as a decision support tool for implementing integrated strategies aimed at reducing setup times in industrial processes. The study seeks to optimize operational efficiency and reduce variability in production processes, ultimately enhancing productivity. A computational model based on Fuzzy Logic was developed to analyze key variables impacting setup times and associated waste. The research was conducted in a company in the Manaus Industrial Park, specializing in electronics. The results showed that the combination of Tools and Equipment with Advanced Planning variables significantly impacts setup time by 86%, highlighting the importance of managing these variables to improve productivity.
Compliance application process as a strategic tool in the management of a third sector organization
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) MELO, Maria Lidelmar Carvalho de; LEITE, Jandecy Cabral; SILVA, Ítalo Rodrigo Soares; SIQUEIRA junior, Paulo Oliveira
Adapting to integrity models that respect the current legislation has become fundamental in the management of organizations as a strategic form, mainly in entities with public and non-profit purposes, in a non-governmental scope. Therefore, the objective is to implement a model for the application of the compliance program in a Third Sector institution. In this way, the present article can be considered in an exploratory, applied and qualitative way, in two aspects, bibliographic research and case study, the data collection was through a meeting and interview with the company's professionals, reporting the importance of the theme. The results showed the main tools and compliance mechanism, proposing actions that can be used in practice with the purpose of providing a broad view of the functioning of the proposed model, with transparency and ethics, thus increasing the competitiveness of the business.
Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; FONSECA JUNIOR, Milton; LEITE, Jandecy Cabral;
With the expansion of means of river transportation, especially in the caseof small and medium-sized vessels that make routes of greater distances, the cost of fuel, if not taken as an analysis criterion for a larger profit margin, is considered to be a primary factor , considering that the value of fuel specifically diesel to power internal combustion machines is high. Therefore, the use of tools that assist in decision making becomes necessary, as is the case of the present research, which aims to contribute with a computational model of prediction and optimization of the best speed to decrease the fuel cost considering the characteristics of the SCANIA 315 machine. propulsion model, of a vessel from the river port of Manaus that carries out river transportation to several municipalities in Amazonas. According to the results of the simulations, the best training algorithm of the Artificial Neural Network (ANN) was the BFGS Quasi-Newton considering the characteristics of the engine for optimization with Genetic Algorithm (AG).
Development of a Prototype Gateway for Data Collection and Transmission in Material Handling Systems - Gate Move 4.0
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-06-09) AMARAL, Carlos Henrique; LEITE, Jandecy Cabral; RIBEIRO, Paulo Francisco da Silva; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; DIRANE, Eduardo Nunes; MENDONÇA, Pedro Henrique Barros
The article discusses the development of a gateway prototype named Gate Move 4.0, designed for data collection and transmission in material handling systems. The goal of the study is to improve operational efficiency and decision-making in industries, especially with the advent of Industry 4.0. The prototype was evaluated under various operating conditions, demonstrating efficiency in data collection, connectivity, and performance in adverse environments. The methodology included requirements analysis and the implementation of sensor technologies and communication protocols. Results showed that Gate Move 4.0 is a viable solution for material handling systems.
Modelo de previsão do KPI confiabilidade em um grupo de máquinas de combustão interna utilizando técnicas de redes neurais artificiais em usinas termoelétricas
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-07) SILVA, Ítalo Rodrigo Soares; NASCIMENTO, Manoel Henrique Reis
The unavailability of equipment in thermoelectric plants for any reason becomes a risk of the entrepreneur, who as a consequence bears even greater losses with a high cost of machines stopped, in addition to the penalties sanctioned and provided by law, based on this assumption the maintenance programs are methodologies that aim to contribute with techniques and tools to mitigate this problem, however, However, only the use of maintenance programs are not enough, thus, this research aims to develop an Engine Reliability Prediction Algorithm, capable of predicting the Reliability Key Performance Indicator, with the purpose of indicating the probability of the equipment to operate in a pre-defined space of time, as the object of study has a group of internal combustion machines of Thermoelectric Power Plants. In view of this, the research meets the objectives of cataloging the significant variables for the prediction model; analyze twelve ANN training algorithms, considering the supervised learning approach, where the number of neurons, hidden layers, and activation functions are performance requirements of the network; To develop the prediction model for the reliability of the motor group, where the training algorithms are validated using the best model stopping criterion; to find the best network performance based on Mean Squared Error (MSE), Root Mean Square Error (RMSE), Linear Regression, and Best Model stopping criterion; and finally, to simulate the cataloged failure data in order to analyze the technical state of the motor group with the best model. The innovation of the research is characterized by the computational methods of data processing by using: optimization methods, iterative and heuristic, characterizing the use of artificial intelligence techniques to predict the reliability in days and months, in addition, it is used predictive maintenance indicators as: Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), Availability and Reliability. To analyze the results of this research, a set of twenty load generation units was used as parameters for investigating the frequency of failures, the twelve training algorithms were applied, with a combination between the activation functions: Sigmoid, Linear and Hyperbolic Tangent, the research results show that the techniques of Levenberg-Marquardt and Bayesian Regularization showed 100% correlation between the output and simulated variables, characterizing the efficiency in predicting in days and months.