Principal components in multivariate control charts applied to data instrumentation of DAMS

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Published: Mar 1, 2016
DOI: https://doi.org/10.14807/ijmp.v7i1.369
Keywords:
Statistical quality control multivariate control charts principal component analysis dam safety.

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Emerson Lazzarotto
Universidade Estadual do Oeste do Paraná - UNIOESTE
Liliana Madalena Gramani
Universidade Federal do Paraná
Anselmo Chaves Neto
Universidade Federal do Paraná
Luiz Albino Teixeira Junior
Universidade Federal da Integração Latino Americana - UNILA

Abstract

Hydroelectric plants are monitored by a high number of instruments that assess various quality characteristics of interest that have an inherent variability. The readings of these instruments generate time series of data on many occasions have correlation. Each project of a dam plant has characteristics that make it unique. Faced with the need to establish statistical control limits for the instrumentation data, this article makes an approach to multivariate statistical analysis and proposes a model that uses principal components control charts and statistical and to explain variability and establish a method of monitoring to control future observations. An application for section E of the Itaipu hydroelectric plant is performed to validate the model. The results show that the method used is appropriate and can help identify the type of outliers, reducing false alarms and reveal instruments that have higher contribution to the variability.

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Article Details

Issue
Vol. 7 No. 1 (2016): Independent Journal of Management & Production
Section
Articles

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Author Biographies

Emerson Lazzarotto, Universidade Estadual do Oeste do Paraná - UNIOESTE

Centro de Engenharias e Ciências Exatas

Campus de Foz do Iguaçu

Liliana Madalena Gramani, Universidade Federal do Paraná

Departamento de Matemática

Anselmo Chaves Neto, Universidade Federal do Paraná

Departamento de Estatistica

Luiz Albino Teixeira Junior, Universidade Federal da Integração Latino Americana - UNILA

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