Predictive as well as preventive maintenance are tools of maintenance programs that aim to increase or maintain the life expectancy of an equipment through computational techniques and tools. Bearing in mind that the power generation industry has a high maintenance rate with machines and / or electric generators stopped, this research aims to develop a computational model for predicting the Reliability Key Performance Indicator (KPI) to identify how available the equipment will be in a time span of 22 days, for this the methodology to be used will be based on analyzes and tests of artificial neural network (ANN) architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the layers hidden to find the best state of convergence and the minimum Root Mean Square Error (RMSE) value calculated between the real and simulated outputs. According to the results obtained by the training, validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation between the real and simulated values, thus the model is able to identify which machine will have the greatest efficiency and less efficiency within the defined time span.
This work of completion aimed to demonstrate the importance of the use of PPE in construction with awareness and guiding the use of personal protective equipment at the construction site area. It is estimated that the construction activity is responsible for many accidents, by requiring its employees to expose themselves to environmental hazards, such as: physical, chemical, biological, ergonomic and accidents. Considering that the Personal Protective Equipment PPE are important security items from working, since, in construction using the same becomes sporadic or non-existent on the part of service providers and employees, even if they have knowledge of the law and standards. During the study we can observe and note that the use of PPE is very important and essential in reducing the risks of accidents during the execution of the activities and their functionality, because the protection of the physical integrity and health of the employee is not isolated behavior, and these must have training and technical guidance on the appropriate use of personal protective equipment. It is understood that the lack of planning of a competent security system can cause occurrences of accidents, causing losses for low productivity and increased costs. Thus, it is evident the importance and prominence that has gained the use of PPE in construction, because this is a topic that has been evolving and requires continuous recycling of professionals in this area.
Due to the high demand for electricity in the manufacturing industry, companies to obtain greater profitability on their produced goods, seek and adopt ways to reduce energy consumption, and use predictive maintenance as a tool by applying thermography. Thus, the purpose of the research is to show the importance of thermographic analysis for assessing losses and preserving the safety of the company's physical facilities. The research is descriptive, qualitative and case study. The instrument used for data collection were direct observation and document analysis. In this context, the results obtained were the mapping in the manufacturing facilities and the identification of some failures in the company's electrical system. After this data collection process, it was possible to analyze and plan the corrective actions. In conclusion, it is possible to reduce manufacturing costs through predictive maintenance through the thermographic analysis tool, positively impacting the company's financial results.
Analisar métodos de processamento de dados e garantir resultados coerentes é um trabalho árduo para os cientistas, o fato é que a otimização de um processo faz a diferença independente de qual área esteja sendo trabalhada. Existem ferramentas que consolidam estudos como esse, uma delas é o método comparativo utilizando análise de dispersão que foi utilizado na presente pesquisa ao trabalhar com o aplicativo Aventureiros Escolares que é o um dos objetos de estudo, com ele foi analisado o agente de inferências denominado Fuzzylite. O estudo de caso foi uma boa opção para a pesquisa ao analisar os métodos de inferência modelados no app e realizar uma comparação com o Toolbox Fuzzy do MatLab®, com isso o objetivo da presente pesquisa é analisar as duas ferramentas citadas e apontar qual delas teve o melhor desempenho utilizando a mesma modelagem Fuzzy. Por meio da análise de dispersão foi possível identificar um melhor resultado para o modelo empregado no Fuzzylite.
This paper presents the use of games as a strategy for teaching the principles of environmental education, focusing on preserving the rivers and streams of Manaus, polluted daily. The research is qualitative, using indirect documentation for data collection, based on articles and websites that address the subject. After the survey were listed the main points about environmental education, water pollution and how to use digital games for environmental awareness. The concepts were applied in the construction of a game prototype that warns about the importance of river preservation and how to avoid pollution. The aim is to achieve this through the simple and dynamic language that games can offer, while at the same time alerting about this environmental cause, focusing on children from six to ten years old, because they have more interaction with the games and are in their age of critical opinion formation. Research has found that water is an important resource, but it is still not preserved as it should. Nevertheless, the combination of gaming entertainment with environmental awareness concepts has proved to be a good alternative to combat the problem.
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