Detecção de outlier como suporte para o controle estatístico do processo multivariado: um estudo de caso em uma empresa do setor plástico.

• Autor(a) principal: Almeida Júnior, José de
• Data de Publicação: 2013
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/5225
• topic: Engenharia de Produção; Controle estatístico da qualidade; Análise de componentes principais; Algoritmo de busca sucessiva; Algoritmo BACON; Statistical quality control; Hotelling T square chart; Principal components analysis; Forward search algorithm; BACON algorithm; ENGENHARIAS::ENGENHARIA DE PRODUCAO

The research project studied, aimed to apply a forward search algorithm to aid decision making in multivariate statistical process control in the manufacture of crates in a company of plastic products. Besides, the use of principal components analysis (PCA) and the Hotelling T square chart can summarize relevant information of this process. Thus, they were produced two results of considerable importance: the scores of the principal components and an adapted Hotelling T square chart, highlighting the relationship between the ten variables analyzed. The forward search algorithm detects discordant points of the data clustering rest that, when are too far away or have very different characteristics, are called outliers. The BACON algorithm was used for the detection of such occurrences, which part of a small subset demonstrably free of the original data outliers and it goes adding new information, which is not outliers, to this initial subset until no information can more be absorbed. One of the advantages of using this algorithm is that it combats the masking and swamping phenomena that alter the mean and covariance estimates. The research results showed that, for the dataset studied, the BACON algorithm did not detected no dissenting point. A simulation was then developed, using a uniform distribution by obtaining random numbers within a range for modifying the mean and standard deviation values, in order to show that this method is effective in detecting these outliers. For this simulation, they were randomly changed 5% of the mean and the standard deviation values of the original data. The result of this simulation showed that the BACON algorithm is perfectly applicable to this case study, being indicated its use in other processes that simultaneously depend on several variables.