Navegando por Autor "Silva, Elias Jeferson de Melo"
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Dissertação Estudo comparativo do desempenho de gráficos de controle EWMA com uso do modelo GMTD para processos autocorrelacionados(Universidade Federal do Rio Grande do Norte, 2019-02-21) Silva, Elias Jeferson de Melo; Vivacqua, Carla Almeida; https://orcid.org/0000-0002-4058-8289; http://lattes.cnpq.br/4339735174795014; http://lattes.cnpq.br/2552273742119114; Gonzalez, Mário Orestes Aguirre; Pinho, André Luis Santos de; Lee, LindaStatistical quality control is one of the most important knowledge fronts of quality engineering. Its main objective is the systematic reduction of the variability in the characteristics of the product or process. One of the ways to analyse these characteristics is through the control charts, which allows the monitoring of the process. However, some assumptions are necessary for its use, as is the independence between observations. However, in practice, many processes do not fulfil this assumption and end up generating an autocorrelated process. This autocorrelation, if not noted, can lead to an increase in false alarm numbers, thus unnecessarily stopping the process several times and leading to increased operating costs. Standard control charts, in general, do not involve the calculation of autocorrelation. An ideal graph to monitor small process shifts and even some autocorrelation processes is the exponentially weighted moving average, or simply EWMA. It is also desirable to use a template that allows monitoring, data series, or process, regardless of whether such data is presented linearly or non-linearly. A model that presents itself as an alternative to this situation is Transition Distribution and Gaussian Mixture, GMTD. So the purpose of this paper was to find a way to combine the advantages of the EWMA control chart with the GMTD model and compare it with the adjustments found in the academic literature to deal with autocorrelation. Such a comparison was made by the performance of such graphs, in general the most usual measure for comparison of the control graphs is by means of the average run length, ARL. The data used in the comparison of performance, and consequently the adopted research method, came from computational simulation, generated in the software. At the end of this stage, conclusions were made that showed the best of the graphs to deal with some autocorrelation scenarios.