Monitoramento de perfis lineares

• Autor(a) principal: Fernandes, Viviany Leão
• Data de Publicação: 2009
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/13826
• topic: Process control statistical methods; Quality control statistical methods; Regression analysis; Least squares; Kalman filtering; Computer modeling; Controle de processo - Métodos estatísticos; Controle de qualidade - Métodos estatísticos; Análise de regressão; Mínimos quadrados; Kalman, Filtragem de; Simulação (Computadores); CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA

Shewhart chart is a fundamental tool in statistical process control, and is useful in the monitoring of key quality characteristics in production processes. That monitoring can be done by univariate or by multivariate control charts, when the quality characteristic can be represented by a random variable or random vector. There are however certain cases where the quality can be represented by a profile, linear or nonlinear, between its characteristics. This work is dedicated to the control strategy for Phase II, to the monitoring of variables in a production process following a linear profile and the regression coefficients estimated by least squares and by Kalman filter. Our aim is to compare the performance of the 2 c control chart when the parameters of the model are estimated by those alternative techniques. Control chart 2 c has been used to monitor parameters of simple linear regression model. It has been proposed to apply the Kalman Filter estimates in the control chart 2 c and to analyse the efficiency of this chart considering such estimates, as well as, the comparison with the least squares estimates. The performance of this chart has been compared by those two techniques of estimation and has been confirmed that the control chart 2 c is more efficient when combined with the Filter Kalman estimates than with the least squares estimates.