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Monitoring univariate process mean with a np control chart with estimated parameters

Grant number: 20/14591-4
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): March 01, 2021
Effective date (End): January 31, 2022
Field of knowledge:Engineering - Production Engineering
Principal researcher:Marcela Aparecida Guerreiro Machado de Freitas
Grantee:Carlos Alberto Santos Rosa Júnior
Home Institution: Faculdade de Engenharia (FEG). Universidade Estadual Paulista (UNESP). Campus de Guaratinguetá. Guaratinguetá , SP, Brazil


Control charts can be by variables or by attributes. Most control charts proposed for monitoring the mean of univariate processes use variable inspection. Recent studies have proposed the use of attribute inspection. It is attractive to monitor processes through attributes because it consumes less time and reduces costs . Commonly, process parameters, such as mean and variance, are assumed to be known. In practice, however, the parameters are rarely really known, and it is necessary to estimate them. The construction of the control chart takes place in two phases: Phase I, where the parameters are estimated in the under control condition and Phase II, where the process is monitored. The results found in the literature show that the parameter estimation significantly affects the performance of the control charts when compared under the same conditions with known parameters. In this work, the control chart by np attributes will be studied for monitoring the mean of univariate processes when the parameters are estimated. The proposed chart will be compared with those in the literature, in terms of the speed with which they signal a disturbance in the process. The properties of the chart will be obtained through simulations, using the R language. The results of the simulations will be used in the validation of the theoretical results to be obtained by the research group of which the advisor is part. In addition, a Web app, using the R package entitled Shiny, will be developed so that the general public, free of charge and without the need to install programs, insert the data of a process to monitor it. Low-cost monitoring tools, available through app, is a trend in the era of Industry 4.0.

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