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Control charts for monitoring multivariate processes

Abstract

The increase in complexity and levels of automation of industrial processes and the availability of computational resources allied to the growing competitive necessity of products with better quality have required from the quality control, the monitoring of several quality characteristics of the product simultaneously. The monitoring strategies, originally proposed to improve the performance of univariate control charts, are being applied for monitoring multivariate processes. In this context, authors have varied the parameters of the multivariate control charts, and / or modified the sampling scheme and / or the run rule. New statistics for monitoring multivariate processes, simpler than those found in the literature and, moreover, more efficient, have been proposed by the applicant and well received by the scientific community, which has already approved several articles of the applicant in national and international journals. Based on the discussions above, the applicant intends to continue the research started in her doctoral, supported by FAPESP, project 2006/00491-0, through the proposition of new monitoring strategies and statistics, always aiming to improve the properties of the control charts for monitoring multivariate processes, especially for autocorrelated processes. This way, the applicant intends to create a center of research on Processes Control in the Production Department of the UNESP - Guaratinguetá. (AU)

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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MACHADO, MARCELA A. G.; COSTA, ANTONIO F. B. Some Comments Regarding the Synthetic (X)over-bar Chart. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, v. 43, n. 14, p. 2897-2906, 2014. Web of Science Citations: 13.
ANTÔNIO FERNANDO BRANCO COSTA; MARCELA APARECIDA GUERREIRO MACHADO. Monitoring the mean vector and the covariance ­matrix of multivariate processes with sample means and sample ranges. Production, v. 21, n. 2, p. -, Jun. 2011.

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