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This article presents a proposal of the method of monitoring complex multidimensional processes. The problem relates to monitoring the quality of production with some attribute variables when the production is performed by some operators. To describe the quality status we used the matrix in which elements are the numbers of defective units.The proposed method uses permutation tests. The "out-of-order" signal is obtained by comparing the matrix in period t to the matrix from stable process. The test statistic used in permutation test is based on a function of distance between matrices. The properties of the proposed method have been described using computer simulation. W artykule przedstawiono propozycję metody monitorowania złożonych wielowymiarowych procesów produkcyjnych. Rozważany problem dotyczy monitorowania jakości produkcji przy stosowaniu oceny alternatywnej jednocześnie względem wielu charakterystyk, gdy produkcja wykonywana jest na wielu różnych stanowiskach. Do opisu stanu jakości w czasie t wykorzystywana jest macierz, w której elementami są liczby wadliwych sztuk wykonanych na danym stanowiskuwedług ocenianych wielu charakterystyk.Proponowana metoda odwołuje się do testu permutacyjnego. Sygnał o nieprawidłowym przebiegu produkcji jest uzyskiwany na podstawie porównania macierzy z bieżącego okresu dla monitorowanego procesu oraz macierzy danych uzyskanej z procesu ustabilizowanego. Ze względu na dużą liczbę charakterystyk rejestrowanych na skali porządkowej konstrukcja statystyki testowej została oparta o funkcję odległości macierzy. Własności proponowanej metody zostały poddane analizie z wykorzystaniem symulacji komputerowych. Przeprowadzono również porównania wyników uzyskanych z zastosowaniem proponowanej metody i karty kontrolnej c.

Submitted by Guilherme Lemeszenski (guilherme@nead.unesp.br) on 2013-08-22T18:59:01Z No. of bitstreams: 1 S0103-65132011000200003.pdf: 742758 bytes, checksum: 46927c8894928707c23dc49d64458c5c (MD5) Made available in DSpace on 2013-08-22T18:59:01Z (GMT). No. of bitstreams: 1 S0103-65132011000200003.pdf: 742758 bytes, checksum: 46927c8894928707c23dc49d64458c5c (MD5) Previous issue date: 2011-06-01 Made available in DSpace on 2013-09-30T19:55:23Z (GMT). No. of bitstreams: 2 S0103-65132011000200003.pdf: 742758 bytes, checksum: 46927c8894928707c23dc49d64458c5c (MD5) S0103-65132011000200003.pdf.txt: 47900 bytes, checksum: d3eba6972839bd1d936480ed7b5e4a2f (MD5) Previous issue date: 2011-06-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T15:14:25Z No. of bitstreams: 2 S0103-65132011000200003.pdf: 742758 bytes, checksum: 46927c8894928707c23dc49d64458c5c (MD5) S0103-65132011000200003.pdf.txt: 47900 bytes, checksum: d3eba6972839bd1d936480ed7b5e4a2f (MD5) Made available in DSpace on 2014-05-20T15:14:25Z (GMT). No. of bitstreams: 2 S0103-65132011000200003.pdf: 742758 bytes, checksum: 46927c8894928707c23dc49d64458c5c (MD5) S0103-65132011000200003.pdf.txt: 47900 bytes, checksum: d3eba6972839bd1d936480ed7b5e4a2f (MD5) Previous issue date: 2011-06-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Os gráficos conjuntos de e R e S² são os mais utilizados para o monitoramento da média e da dispersão do processo. Com os tamanhos de amostra usuais de 4 e 5, os gráficos de R em uso conjunto são ligeiramente inferior aos gráficos de e S² em uso conjunto em termos da eficiência em detectar alterações no processo. Neste artigo, mostra-se que para o caso multivariado, os gráficos baseados nas médias amostrais padronizadas e amplitudes amostrais (gráfico MRMAX) ou nas médias amostrais padronizadas e variâncias amostrais (gráfico MVMAX) são similares em termos da eficiência em detectar alterações no vetor de médias e/ou na matriz de covariâncias. A familiaridade do usuário com o cálculo de amplitudes amostrais é um aspecto favorável do gráfico MRMAX. Um exemplo é apresentado para ilustrar a aplicação do gráfico proposto. The joint and S² charts are the most common charts used for monitoring the process mean and dispersion. With the usual sample sizes of 4 and 5, the joint and R charts are slightly inferior to the joint and S² charts in terms of efficiency in detecting process shifts. In this article, we show that for the multivariate case, the charts based on the standardized sample means and sample ranges (MRMAX chart) or on the standardized sample means and sample variances (MVMAX chart) are similar in terms of efficiency in detecting shifts in the mean vector and/or in the covariance matrix. User's familiarity with the computation of sample ranges is a point in favor of the MRMAX chart. An example is presented to illustrate the application of the proposed chart. UNESP UNESP

One approach for monitoring autocorrelated data consists in applying a control chart to the residuals of a time series model. However, due to the so called “forecast recovery”, the response to a mean shift in the observed process can appear attenuated in the residual series, in particular, after a short transient phase. To try to overcome this problem, we suggest a simple modification of the standard residual Multivariate Exponentially Weighted Moving Average (MEWMA) control chart which reduces the “forecast recovery” effect. Comparisons, based on two real industrial process models, show that the proposed modification can enhance the ability of the MEWMA control chart to detect both small and medium mean shifts.