Revisão das cartas de controle multivariadas paramétricas

The current industrial processes with industrial automation and a high volume of data inherent to the processes have required the control of variables in real time, so you can get quick answers to the detection and correction of failures that have occurred during the process. The multivariate nature of the industrial process requires robust methods to obtain effective statistical control. Parametric Multivariate Control charts (CCMP) are widely used in the industrial sector for the monitoring and process control. Parametric multivariate control charts are traditional control charts that presupposes the knowledge of the distribution of variables, for the application of the methods found in the literature. In this article, we discuss the main procedures of CCMP found in literature: Hotelling T ², MEWMA and MCUSUM. As well as its applications in industrial processes. Considering the publications held between 2006 and 2016, surveyed in the main databases of scientific analysis. This article emphasizes the need for review articles, since they represent compiled expositions, inclined to the triggering of new ideas and fields of research. Thus, it is expected that the presented work can serve as a source of motivation for the elaboration of new studies and adaptations of the implementations discussed here.Os processos industriais atuais, dotados de automação industrial e de um alto volume de dados inerentes aos processos têm exigido o controle das variáveis em tempo real, para que seja possível obter respostas rápidas à detecção e correção de falhas ocorridas durante o processo. A natureza multivariável dos processos industriais exigem métodos mais robustos para se obtenha um controle estatístico efetivo. As Cartas de Controle Multivariadas Paramétricas são amplamente utilizadas no setor industrial para o monitoramento e controle de processos. As Cartas de Controle Multivariadas Paramétricas são cartas de controle tradicionais que pressupõe o conhecimento da distribuição das variáveis, para aplicação dos métodos encontrados na literatura. Neste artigo, discutimos os principais procedimentos de CCMP encontrados na literatura: Hotelling T², MEWMA e MCUSUM. Assim como suas aplicações nos processos industriais. Considerando as publicações realizadas entre 2006 e 2016, pesquisadas nas principais bases de dados de análise científica. Enfatiza-se neste artigo a necessidade de artigos de revisão, pois estes representam estudos expostos de forma compilada inclinando-se ao desencadeamento de novas ideias e campos de pesquisa. Assim, espera-se que o trabalho apresentado possa servir como fonte de motivação para elaboração de novos estudos e adaptações das implementações aqui discutidas

A study of advanced control charts for complex time-between-events data

Ph.DDOCTOR OF PHILOSOPH

Multivariate process variability monitoring for high dimensional data

In today’s competitive market, the quality of a product or service is no longer measured by a single variable but by a number of variables that define the quality of the final product or service. It is known that these quality variables of products or services are correlated with each other, and it is therefore important to monitor these correlated quality characteristics simultaneously. Multivariate quality control charts are capable of such monitoring. Multivariate monitoring of industrial or clinical procedures often involves more than three correlated quality characteristics, and the status of the process is judged using a sample of one size. The majority of existing control charts for monitoring multivariate process variability for individual observations are capable of monitoring up to three quality characteristics. One of the hurdles in designing optimal variability control charts for large dimension data is the enormous computing resources and time that is required by the simulation algorithm to estimate the charts parameters. In this research, a novel algorithm based on the parallelised Monte Carlo simulation has been developed to improve the ability of the Multivariate Exponentially Weighted Mean Squared Deviation (MEWMS) and Multivariate Exponentially Weighted Moving Variance (MEWMV) charts to monitor multivariate process variability with a greater number of quality characteristics. Different techniques have been deployed to reduce computing space and the time complexity taken by the algorithm. The novelty of this algorithm is its ability to estimate the optimal control limit L (optimal L) for any given number of correlated quality characteristics, size of the shifts to be detected based on the smoothing constant, and the given in-control average run length in a computationally efficient way. The optimal L for the MEWMS and MEWMV charts to detect small, medium and large shifts in the covariance matrix of up to fifteen correlated quality characteristics has been provided. Furthermore, utilising the large number of optimal L values generated by the algorithm has enabled us to develop two mathematical functions that are capable of predicting L values for MEWMS and MEWMV charts. This would eliminate the need for further execution of the parallelised Monte Carlo simulation for high dimension data. One of the main challenges in deploying multivariate control charts is to identify which characteristics are responsible for the out-of-control signal detected by the charts, and what is the extent of their contribution to the signal. In this research, a smart diagnostic technique has been developed by using a hybrid of the wrapper filter approach to effectively identify the variables that are responsible for the process faults and to classify the percentage of their contribution to the faults. The robustness of the proposed techniques has been demonstrated through their application to a range of clinical and industrial multivariate processes where the percentage of correct classifications is presented for different scenarios. The majority of the existing multivariate control charts have been developed to monitor processes that follow multivariate normal distribution. In this thesis, the author has proposed a control chart for a non-normal high dimensional multivariate process based on the percentile point of Burr XII distribution. Geometric distance variables are fitted to the subset of correlated quality characteristics to reduce the dimension of the data, which is then followed by fitting the Burr XII distribution to each geometric distance variable. Since individual distance variables are independent, each can be monitored by individual control charts based on the percentile points of the fitted Burr XII distributions. A simulated annealing approach is used to estimate parameters of the Burr XII distribution. The proposed hybrid is utilised to identify and rank the variables responsible for the out-of-control signals of geometric distance variables