Gráficos de controle multivariados para monitoramento de processos não lineares em bateladas
Multivariate control charts for monitoring non-linear batch processes
Marcondes Filho, Danilo; Fogliatto, Flavio Sanson; Oliveira, Luiz Paulo Luna de
http://dx.doi.org/10.1590/S0103-65132011005000006
Prod, vol.21, n1, p.132-148, 2011
Resumo
Processos industriais em bateladas são empregados com frequência na produção de certos itens. Tais processos disponibilizam uma estrutura de dados peculiar; diante disso, existe um crescente interesse no desenvolvimento de gráficos de controle multivariados mais apropriados para seu monitoramento. Investiga-se aqui uma abordagem recente que utiliza gráficos de controle baseados no método Statis. O Statis constitui-se em uma técnica exploratória que permite avaliar similaridade entre matrizes de dados. Entretanto, essa técnica considera a similaridade em um contexto linear, investigando estruturas de correlação lineares nos dados. Propõe-se neste artigo a utilização de gráficos de controle baseados no Statis em conjunto com kernels para monitoramento de processos com presença de não linearidades fortes. Através dos kernels, definem-se funções não lineares dos dados para melhor representação da estrutura a ser caracterizada pelo método Statis. Essa nova abordagem, denominada Kernel-Statis, é desenvolvida e avaliada utilizando dados de um processo simulado.
Palavras-chave
Controle multivariado da qualidade. Gráficos de controle. Processos em bateladas. Kernel. Método Statis.
Abstract
Industrial batch processes are widely used in the production of certain items. Such processes provide a peculiar data structure; therefore there is a growing interest in the development of customized multivariate control charts for their monitoring. We investigate a recent approach that uses control charts based on the Statis method. Statis is an exploratory technique for measuring similarities between data matrices. However, the technique only assesses similarities in a linear context, i.e. investigating structures of linear correlation in the data. In this paper we propose control charts based on the Statis method in conjunction with a kernel for monitoring processes in the presence of strong nonlinearities. Through kernels we define nonlinear functions of data for better representing the structure to be characterized by the Statis method. The new approach, named Kernel-Statis, is developed and illustrated using simulated data.
Keywords
Multivariate quality control. Control charts. Batch processes. Kernel. Statis method.
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