Production
https://prod.org.br/article/doi/10.1590/S0103-65132005000100007
Production
Article

Identificação de variáveis fora de controle em processos produtivos multivariados

Identification of out-of-control variables in a multivariate productive process

Souza, Adriano Mendonça; Rigão, Maria Helena

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Resumo

A maioria dos processos produtivos, envolvendo múltiplas variáveis, geram grande quantidade de dados, nos quais o monitoramento simultâneo se faz necessário. Assim, o objetivo principal deste trabalho é mostrar, por meio de um exemplo, um procedimento de identificação de variáveis responsáveis pela instabilidade do processo. Ele considera variáveis fraca e fortemente correlacionadas. O comportamento destas variáveis é analisado, inicialmente, por meio do gráfico de controle T2 de Hotelling, para a verificação da estabilidade do processo. Quando o processo é considerado fora de controle, uma investigação adicional é realizada. Para as variáveis fracamente correlacionadas o gráfico X-barra, com limites de Bonferroni, é utilizado. Para as variáveis com forte correlação, os dados são decompostos em componentes principais e estas são analisadas pelo gráfico de controle X-barra. Em ambos os casos, as técnicas utilizadas identificaram corretamente a variável responsável pela instabilidade do processo, bem como o período do distúrbio.

Palavras-chave

Controle estatístico de processos, Gráfico de controle T2 de Hotelling, Gráfico X-barra com limites de Bonferroni, Análise de componentes principais

Abstract

Most productive processes are affected by multiple variables and generate a great amount of data and simultaneous monitoring becomes necessary. Thus, the main purpose of this work is to show, by means of an example, procedures of identification of variables responsible for process instability. It considers weakly and strongly correlated variables. The behavior of these variables is analyzed, initially by means of a Hotteling`s T2 chart for the verification of the process stability. When the process is considered to be out-of-control, another investigation is carried out. For variables of weak correlation an X-bar chart with Bonferroni limits is used. For variables of strong correlation, the data are decomposed in principal components and analyzed by means of a X-bar control chart. In both cases, the techniques used can correctly identify the variable responsible for the process instability, as well as the period of disturbance.

Keywords

Statistical process control, Hotelling's T2 chart, X-bar chart with Bonferroni limits, Principal components analysis

References



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