Gráficos de controle multivariados para monitoramento de processos não lineares em bateladas

Abstract

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.

Systematic literature review of Brazilian research on multivariate control charts

“…Qiu (2018) believes that control charts are a quality management tool because, besides contemplating technical aspects, they also provide an organizational environment for continuous improvement. According to Marcondes Filho, Fogliatto and Oliveira (2011) and Wang et al (2018), control charts are quantitative management tools used to support statistical control of product quality in the production process, reducing out-of-specification product volume and costs. related to production.…”

“…improved productivity through reduction of rework, prevention of defects, effective worker participation, availability of information about the process, as well as its productive capacity (MONTGOMERY, 2016;MARCONDES FILHO, FOGLIATTO;OLIVEIRA, 2011;FAISAL et al, 2018). Maboudou-Tchao, Silva and Diawara (2018) believe that the processes of industrial manufacturing modernization contemplate several situations that require the simultaneous monitoring of the quality characteristics involved, and the separate control of these characteristics can lead to interpretations mistakes about such quality.…”

Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO

“…STATIS developments have had important and numerous contributions and can be classified according to the input data (same variables or individuals in the k-matrices), according to the weights assigned to the compromised matrix, when considering external information and if pairs of matrices are available in different situations or times. These methods started with the STATIS and STATIS-DUAL [22], to be continued with X-STATIS or Triadic Partial Analysis (PTA) [24], STATIS-CoA [25], STATICO (STATIS and COINERCIA) [26,27], DOUBLE STATIS (DO-ACT) [28], K + 1 STATIS [29], DIS-TATIS [30], STATIS-4 [31], Kernel-STATIS [32], COVSTATIS and COSTATIS (COINER-CIA and STATIS) [33], POWER-STATIS [34], Anisotropic STATIS or ANISOSTATIS [35], STATIS-LDA [36], INTERSTATIS [37], Sir-STATIS [38], HiDISTATIS and DiDISTATIS [39], CATATIS [40], STATICO-CoA [41] and recently CLUSTATIS [42].…”

Proposal of the Dichotomous STATIS DUAL Method: Software and Application for the Analysis of Dichotomous Data, Applied to the Test of Learning Styles in University Students

“…In the literature, there are many statistical methods that allow data analysis of multiple tables, as it is the case of the methods of the STATIS family, which analyze information with the characteristic of being organized in data cubes with the same individuals and the same variables at different times; or the same individuals with different variables at different times; or different individuals with the same variables at different times. It is important to mention that one of the main characteristics of the STATIS family is the detection of the common information shared by the data tables; for example: STATIS and STATISdual [1], X-STATIS or PTA [2], STATICO [3], Double-STATIS (Do-ACT) [4], (k + 1) STATIS or external STATIS [5], DISTATIS [6], CANOSTATIS or Canonical-STATIS [7], STATIS-4 [8], Kernel-STATIS [9], COVSTATIS [3], COSTATIS [10], Power-STATIS [11], STATIS-LDA [5], INTERSTATIS [12] HiDiSTATIS and DiDiSTATIS [13], CATATIS [14], STATICO-CoA [15], and CLUSTATIS [16], just to mention a few, but none of the previous ones in the meaning of CATATIS analyze dichotomous data. For that reason, the STATIS DUAL Dichotomous method is proposed, which allows the analysis of different tables that have the same variables in columns, with different individuals or observations in rows, to detect the common information shared by the different data tables.…”