Shewhart X bar control charts with estimated control limits are widely used in practice. When the sample size is not fixed,we propose seven statistics to estimate the standard deviation sigma . These estimators are applied to estimate the control limits of Shewhart X bar control chart. The estimated results through simulated computation are given and discussed. Finally, we investigate the performance of the Shewhart X bar control charts based on the seven estimators of sigma via its simulated average run length (ARL).

A performance analysis of three different Minimax control charts is performed with respect to their Chi-Square control chart counterparts under several different conditions. A unique control chart must be constructed for each process described by a unique combination of quality characteristic mean vector and associated covariance matrix. The three different charts under consideration differ in the number of quality characteristic variables of concern. In each case, without loss of generality the in-control quality characteristic mean vector is assumed to have zero entries and the associated covariance matrix is assumed to have non-negative entries. The performance of the Chi-Square and Minimax charts are compared under different values of the sample size, the probability of a Type I error, and selected shifts in the quality characteristic mean vector. Minimax and Chi-Square charts that are compared share identical in-control average run lengths (ARL) making the out-of-control ARL the appropriate performance measure. A combined Tausworthe pseudorandom number generator is used to generate the out-of-control mean vectors. Issues regarding multivariate uniform pseudorandom number generation are addressed. / Master of Science

Even a modest positive autocorrelation results in a considerable increase in the number of false alarms that are produced when applying a CUSUM chart. Knowledge of the process to be controlled allows for suitable adaptation of the CUSUM procedure. If one has to suspect the normality assumption, nonparametric control procedures such as the rank-based CUSUM chart are a practical alternative. The paper reports the results of a simulation study on the robustness (in terms of sensitivity of the ARL) of the rank-based CUSUM chart against serial correlation of the control variable. The results indicate that the rank-based CUSUM chart is less affected by correlation than the observation-based chart: The rank-based CUSUM chart shows a smaller increase in the number of false alarms and a higher decrease in the ARL in the out-of-control case than the the observation-based chart. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

As time passes, change occurs. With this change comes the need for surveillance. One may be a technician on an assembly line and in need of a surveillance technique to monitor the number of defective components produced. On the other hand, one may be an administrator of a hospital in need of surveillance measures to monitor the number of patient falls in the hospital or to monitor surgical outcomes to detect changes in surgical failure rates. A natural choice for on-going surveillance is the control chart; however, the chart must be constructed in a way that accommodates the situation at hand. Two scenarios involving attribute control charting are investigated here. The first scenario involves Poisson count data where the area of opportunity changes. A modified exponentially weighted moving average (EWMA) chart is proposed to accommodate the varying sample sizes. The performance of this method is compared with the performance for several competing control chart techniques and recommendations are made regarding the best preforming control chart method. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). The second scenario involves monitoring a process where items are classified into more than two categories and the results for these classifications are readily available. A multinomial cumulative sum (CUSUM) chart is proposed to monitor these types of situations. The multinomial CUSUM chart is evaluated through comparisons of performance with competing control chart methods. This research is a result of joint work with Mr. Lee J. Wells (Grado Department of Industrial and Systems Engineering, Virginia Tech) and Dr. William H. Woodall (Department of Statistics, Virginia Tech). / Ph. D.

The risk-adjusted Bernoulli cumulative sum (CUSUM) chart developed by Steiner et al. (2000) is an increasingly popular tool for monitoring clinical and surgical performance. In practice, however, use of a fixed control limit for the chart leads to quite variable in-control average run length (ARL) performance for patient populations with different risk score distributions. To overcome this problem, the simulation-based dynamic probability control limits (DPCLs) patient-by-patient for the risk-adjusted Bernoulli CUSUM charts is determined in this study. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, the risk-adjusted CUSUM charts with DPCLs have consistent in-control performance at the desired level with approximately geometrically distributed run lengths. Simulation results demonstrate that the proposed method does not rely on any information or assumptions about the patients' risk distributions. The use of DPCLs for risk-adjusted Bernoulli CUSUM charts allows each chart to be designed for the corresponding particular sequence of patients for a surgeon or hospital. The effect of estimation error on performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is also examined. Our simulation results show that the in-control performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is affected by the estimation error. The most influential factors are the specified desired in-control average run length, the Phase I sample size and the overall adverse event rate. However, the effect of estimation error is uniformly smaller for the risk-adjusted Bernoulli CUSUM chart with DPCLs than for the corresponding chart with a constant control limit under various realistic scenarios. In addition, there is a substantial reduction in the standard deviation of the in-control run length when DPCLs are used. Therefore, use of DPCLs has yet another advantage when designing a risk-adjusted Bernoulli CUSUM chart. These researches are results of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). Moreover, DPCLs are adapted to design the risk-adjusted CUSUM charts for multiresponses developed by Tang et al. (2015). It is shown that the in-control performance of the charts with DPCLs can be controlled for different patient populations because these limits are determined for each specific sequence of patients. Thus, the risk-adjusted CUSUM chart for multiresponses with DPCLs is more practical and should be applied to effectively monitor surgical performance by hospitals and healthcare practitioners. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech) and Mr. Justin Loda (Department of Statistics, Virginia Tech). / Ph. D.

Statistical Process Control (SPC), and in particular control charting, is widely used to achieve and maintain control of various processes in manufacturing. A control chart is a graphical display that plots quality characteristics versus the sample number or the time line. Interest in effective implementation of control charts for autocorrelated processes has increased in recent years. However, because of the complexities involved, few systematic design approaches have thus far been developed. Many control charting methods can be viewed as the charting of the output of a linear filter applied to the process data. In this dissertation, we generalize the concept of linear filters for control charts and propose new control charting schemes, the general linear filter (GLF) and the 2nd-order linear filter, based on the generalization. In addition, their optimal design methodologies are developed, where the filter parameters are optimally selected to minimize the out-of-control Average Run Length (ARL) while constraining the in-control ARL to some desired value. The optimal linear filters are compared with other methods in terms of ARL performance, and a number of their interesting characteristics are discussed for various types of mean shifts (step, spike, sinusoidal) and various ARMA process models (i.i.d., AR(1), ARMA(1,1)). Also, in this work, a new discretization approach for substantially reducing the computational time and memory use for the Markov chain method of calculating the ARL is proposed. Finally, a gradient-based optimization strategy for searching optimal linear filters is illustrated.

O presente trabalho propõe um método para a aplicação do gráfico de controle de regressão para o monitoramento de processos industriais. O método proposto inclui uma modificação do gráfico de controle de regressão múltipla, permitindo o monitoramento direto da característica de qualidade do processo ao invés do monitoramento dos resíduos padronizados do modelo de regressão, facilitando a interpretação dos operadores do processo. O método é dividido em duas fases principais: (i) Fase I - análise retrospectiva e (ii) Fase II - monitoramento do processo. A Fase I é composta pela coleta das amostras iniciais, estimação do modelo de regressão e análise de estabilidade dos dados coletados e, a partir desta fase, define-se alguns parâmetros a serem utilizados na fase seguinte. A Fase II do método consiste na coleta periódica de amostras, verificação da extrapolação dos valores das variáveis de controle e monitoramento do processo propriamente dito. O método proposto foi validado através da aplicação em um processo produtivo e de uma comparação do número médio de amostras (NMA) do gráfico de controle de regressão proposto, gerado através de simulação de Monte Carlo, com outros procedimentos similares encontrados na literatura. Como principais resultados esta dissertação apresenta: (i) proposta de um método sistematizado para nortear a aplicação de gráficos de controle de regressão; (ii) adaptação do gráfico de controle de regressão, de forma a permitir o monitoramento direto da característica de qualidade; (iii) proposta de um procedimento gráfico para a verificação da extrapolação das variáveis de controle e (iv) obtenção do NMA do gráfico de controle de regressão proposto e de outros procedimentos encontrados na literatura. O método proposto foi aplicado em um processo produtivo de uma indústria de borrachas. / This work proposes a method for the application of regression control charts in the monitoring of industrial processes. In order to facilitate the interpretation by the process operators, a modification in the multiple regression control chart is proposed allowing the direct monitoring of the values of quality characteristic of the process, instead of monitoring the regression standardized residuals. The proposed method is divided into two Phases: (i) Phase I, called retrospective analysis, and Phase II, called process monitoring. Phase I is composed by sampling, estimation of linear regression model and verification of stability of these samples. This phase defines some parameters to be used in the following phase. Phase II consists in periodic sampling of the process, altogether with verification of the extrapolation of process control variables and the process monitoring itself. The proposed method was validated through practical application in an industrial process and compared with other procedures found in literature. This work has also achieved the average run length (ARL) of the proposed regression control chart, which was compared with the other procedures consulted. The main contributions of this work may be pointed: (i) the proposal of a method to guide the application of regression control chart; (ii) the adaptation of the multiple regression control chart, allowing the direct monitoring of the quality characteristic; (iii) the proposal of a control chart to monitor the extrapolation of the process control variable and (iv) the obtaining of the ARL of the proposed regression control chart and other similar procedures. The proposed method was applied in a process of a rubber manufactory.

O presente trabalho propõe um método para a aplicação do gráfico de controle de regressão para o monitoramento de processos industriais. O método proposto inclui uma modificação do gráfico de controle de regressão múltipla, permitindo o monitoramento direto da característica de qualidade do processo ao invés do monitoramento dos resíduos padronizados do modelo de regressão, facilitando a interpretação dos operadores do processo. O método é dividido em duas fases principais: (i) Fase I - análise retrospectiva e (ii) Fase II - monitoramento do processo. A Fase I é composta pela coleta das amostras iniciais, estimação do modelo de regressão e análise de estabilidade dos dados coletados e, a partir desta fase, define-se alguns parâmetros a serem utilizados na fase seguinte. A Fase II do método consiste na coleta periódica de amostras, verificação da extrapolação dos valores das variáveis de controle e monitoramento do processo propriamente dito. O método proposto foi validado através da aplicação em um processo produtivo e de uma comparação do número médio de amostras (NMA) do gráfico de controle de regressão proposto, gerado através de simulação de Monte Carlo, com outros procedimentos similares encontrados na literatura. Como principais resultados esta dissertação apresenta: (i) proposta de um método sistematizado para nortear a aplicação de gráficos de controle de regressão; (ii) adaptação do gráfico de controle de regressão, de forma a permitir o monitoramento direto da característica de qualidade; (iii) proposta de um procedimento gráfico para a verificação da extrapolação das variáveis de controle e (iv) obtenção do NMA do gráfico de controle de regressão proposto e de outros procedimentos encontrados na literatura. O método proposto foi aplicado em um processo produtivo de uma indústria de borrachas. / This work proposes a method for the application of regression control charts in the monitoring of industrial processes. In order to facilitate the interpretation by the process operators, a modification in the multiple regression control chart is proposed allowing the direct monitoring of the values of quality characteristic of the process, instead of monitoring the regression standardized residuals. The proposed method is divided into two Phases: (i) Phase I, called retrospective analysis, and Phase II, called process monitoring. Phase I is composed by sampling, estimation of linear regression model and verification of stability of these samples. This phase defines some parameters to be used in the following phase. Phase II consists in periodic sampling of the process, altogether with verification of the extrapolation of process control variables and the process monitoring itself. The proposed method was validated through practical application in an industrial process and compared with other procedures found in literature. This work has also achieved the average run length (ARL) of the proposed regression control chart, which was compared with the other procedures consulted. The main contributions of this work may be pointed: (i) the proposal of a method to guide the application of regression control chart; (ii) the adaptation of the multiple regression control chart, allowing the direct monitoring of the quality characteristic; (iii) the proposal of a control chart to monitor the extrapolation of the process control variable and (iv) the obtaining of the ARL of the proposed regression control chart and other similar procedures. The proposed method was applied in a process of a rubber manufactory.

O presente trabalho propõe um método para a aplicação do gráfico de controle de regressão para o monitoramento de processos industriais. O método proposto inclui uma modificação do gráfico de controle de regressão múltipla, permitindo o monitoramento direto da característica de qualidade do processo ao invés do monitoramento dos resíduos padronizados do modelo de regressão, facilitando a interpretação dos operadores do processo. O método é dividido em duas fases principais: (i) Fase I - análise retrospectiva e (ii) Fase II - monitoramento do processo. A Fase I é composta pela coleta das amostras iniciais, estimação do modelo de regressão e análise de estabilidade dos dados coletados e, a partir desta fase, define-se alguns parâmetros a serem utilizados na fase seguinte. A Fase II do método consiste na coleta periódica de amostras, verificação da extrapolação dos valores das variáveis de controle e monitoramento do processo propriamente dito. O método proposto foi validado através da aplicação em um processo produtivo e de uma comparação do número médio de amostras (NMA) do gráfico de controle de regressão proposto, gerado através de simulação de Monte Carlo, com outros procedimentos similares encontrados na literatura. Como principais resultados esta dissertação apresenta: (i) proposta de um método sistematizado para nortear a aplicação de gráficos de controle de regressão; (ii) adaptação do gráfico de controle de regressão, de forma a permitir o monitoramento direto da característica de qualidade; (iii) proposta de um procedimento gráfico para a verificação da extrapolação das variáveis de controle e (iv) obtenção do NMA do gráfico de controle de regressão proposto e de outros procedimentos encontrados na literatura. O método proposto foi aplicado em um processo produtivo de uma indústria de borrachas. / This work proposes a method for the application of regression control charts in the monitoring of industrial processes. In order to facilitate the interpretation by the process operators, a modification in the multiple regression control chart is proposed allowing the direct monitoring of the values of quality characteristic of the process, instead of monitoring the regression standardized residuals. The proposed method is divided into two Phases: (i) Phase I, called retrospective analysis, and Phase II, called process monitoring. Phase I is composed by sampling, estimation of linear regression model and verification of stability of these samples. This phase defines some parameters to be used in the following phase. Phase II consists in periodic sampling of the process, altogether with verification of the extrapolation of process control variables and the process monitoring itself. The proposed method was validated through practical application in an industrial process and compared with other procedures found in literature. This work has also achieved the average run length (ARL) of the proposed regression control chart, which was compared with the other procedures consulted. The main contributions of this work may be pointed: (i) the proposal of a method to guide the application of regression control chart; (ii) the adaptation of the multiple regression control chart, allowing the direct monitoring of the quality characteristic; (iii) the proposal of a control chart to monitor the extrapolation of the process control variable and (iv) the obtaining of the ARL of the proposed regression control chart and other similar procedures. The proposed method was applied in a process of a rubber manufactory.

Multivariate control charts are used to monitor a process when more than one quality variable associated with the process is being observed. The multivariate exponentially weighted moving average (MEWMA) control chart is one of the most commonly recommended tools for multivariate process monitoring. The standard practice, when using the MEWMA control chart, is to take samples of fixed size at regular sampling intervals for each variable. In the first part of this dissertation, MEWMA control charts based on sequential sampling schemes with two possible stages are investigated. When sequential sampling with two possible stages is used, observations at a sampling point are taken in two groups, and the number of groups actually taken is a random variable that depends on the data. The basic idea is that sampling starts with a small initial group of observations, and no additional sampling is done at this point if there is no indication of a problem with the process. But if there is some indication of a problem with the process then an additional group of observations is taken at this sampling point. The performance of the sequential sampling (SS) MEWMA control chart is compared to the performance of standard control charts. It is shown that that the SS MEWMA chart is substantially more efficient in detecting changes in the process mean vector than standard control charts that do not use sequential sampling. Also the situation is considered where different variables may have different measurement costs. MEWMA control charts with unequal sample sizes based on differing measurement costs are investigated in order to improve the performance of process monitoring. Sequential sampling plans are applied to MEWMA control charts with unequal sample sizes and compared to the standard MEWMA control charts with a fixed sample size. The steady-state average time to signal (SSATS) is computed using simulation and compared for some selected sets of sample sizes. When different variables have significantly different measurement costs, using unequal sample sizes can be more cost effective than using the same fixed sample size for each variable. In the second part of this dissertation, control chart methods are proposed for process monitoring when the quality of a process or product is characterized by a linear function. In the historical analysis of Phase I data, methods including the use of a bivariate <i>T</i>² chart to check for stability of the regression coefficients in conjunction with a univariate Shewhart chart to check for stability of the variation about the regression line are recommended. The use of three univariate control charts in Phase II is recommended. These three charts are used to monitor the <i>Y</i>-intercept, the slope, and the variance of the deviations about the regression line, respectively. A simulation study shows that this type of Phase II method can detect sustained shifts in the parameters better than competing methods in terms of average run length (ARL) performance. The monitoring of linear profiles is also related to the control charting of regression-adjusted variables and other methods. / Ph. D.