Economic-statistical design of the (X)over-bar chart used to control a wandering process mean using genetic algorithm

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoring a wandering mean with an np chart

Este artigo considera um gráfico np x proposto por Wu et al. (2009) para controle de média de processo como uma alternativa ao uso do gráfico de. O que distingue do gráfico de controle np x é o fato das unidades amostrais serem classificadas como unidades de primeiro ou de segunda classe de acordo com seus limites discriminantes. O gráfico tradicional np é um caso particular do gráfico np x quando os limites discriminantes coincidem com os limites de especificação e unidade de primeira (segunda) classe é um item conforme (não conforme). Estendendo o trabalho de Reynolds Junior, Arnold e Baik (1996), consideramos que a média de processo oscila mesmo na ausência de alguma causa especial. As propriedades de Cadeia de Markov foram adotadas para avaliar o desempenho do gráfico np x no monitoramento de média de processos que oscila. de modo geral, o gráfico np x requer amostras duas vezes maior para superar desempenho do gráfico (enquanto que o gráfico tradicional np necessita tamanho de amostras cinco ou seis vezes maior).

Economic design of two-stage (X)over-bar charts: the Markov chain approach

When the (X) over bar chart is in use, samples are regularly taken from the process, and their means are plotted on the chart. In some cases, it is too expensive to obtain the X values, but not the values of a correlated variable Y. This paper presents a model for the economic design of a two-stage control chart, that is. a control chart based on both performance (X) and surrogate (Y) variables. The process is monitored by the surrogate variable until it signals an out-of-control behavior, and then a switch is made to the (X) over bar chart. The (X) over bar chart is built with central, warning. and action regions. If an X sample mean falls in the central region, the process surveillance returns to the (Y) over bar chart. Otherwise. The process remains under the (X) over bar chart's surveillance until an (X) over bar sample mean falls outside the control limits. The search for an assignable cause is undertaken when the performance variable signals an out-of-control behavior. In this way, the two variables, are used in an alternating fashion. The assumption of an exponential distribution to describe the length of time the process remains in control allows the application of the Markov chain approach for developing the cost function. A study is performed to examine the economic advantages of using performance and surrogate variables. (C) 2003 Elsevier B.V. All rights reserved.

Planejamento econômico de gráficos de controle X para monitoramento de processos autocorrelacionados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model.

Effect of Size Reduction on Magnetic Ordering in Sm1-x Ca (x) MnO3 (x=0.35, 0.65 and 0.92) Manganites: Magnetic and EMR Studies

We have employed the highly sensitive electron magnetic resonance technique complimented by magnetization measurements to study the impact of size reduction on the magnetic ordering in nanosized Sm1-x Ca (x) MnO3 (x = 0.35, 0.65 and 0.92). In the bulk form, x = 0.35 sample shows a charge ordering transition at 235 K followed by a mixed magnetic phase, the sample with x = 0.65 exhibits charge order below 275 K and shows an antiferromagnetic insulator phase below 135 K while that with x = 0.92 has a ferromagnetic-cluster glass ground state. Thus, a comparative study of magnetic ground states of bulk and nanoparticles (diameter similar to 25 nm) enables us to investigate size-induced effects on different types of magnetic ordering. It is seen that in the bulk samples the temperature dependences of the EPR parameters are quite different from each other. This difference diminishes for the nanosamples where all the three samples show qualitatively similar behavior. The magnetization measurements corroborate this conclusion.

Control charts: an application in shrimp canning

Control chart is a statistical tool which can be employed with advantage to learn the situation in the process (whether it is under control or not). There are different kinds of control charts but one which is most commonly used is the control chart for variables, known as X-R chart. This chart can be used for measurable characteristics in food industry like appearance, colour, sizes and dimensions for chemical properties such as moisture, fat and many other analytical counts and measurements. Since construction and maintenance of such charts involve a recognizable amount of time and effort, they should not be used indiscriminately but only where it can be definitely shown that their use improves the overall operation. Since one control chart can be used for only one quality attribute, those for which the charts are used should be selected with care (Kramer and Twigg, 1962). In this article, the procedure of setting up a variable control chart is described with observations taken on filling operation of cans in a shrimp canning factory.

Teste de condutividade elétrica em sementes de amendoim

O teste de condutividade elétrica (CE) tem potencial para ser empregado no controle de qualidade pelas empresas produtoras de sementes, no entanto há fatores que podem afetar os resultados obtidos. Neste sentido, o trabalho teve como objetivo estudar os efeitos do tamanho da semente, do número de sementes da amostra e do período e temperatura de embebição que podem influenciar a interpretação dos resultados do teste de CE, em sementes de amendoim. Utilizou-se o delineamento experimental inteiramente casualizado em esquema fatorial 3 x 3: três lotes de semente, classificados em três tamanhos (peneiras 18, 20 e 22). Avaliou-se a CE em dez tempos de embebição (3, 6, 9, 12, 15, 18, 21, 24, 27 e 30 horas). O trabalho foi composto de quatro ensaios, onde se combinou número de sementes e temperatura de exposição das mesmas à embebição: a) quatro repetições de 25 sementes a 20ºC; b) quatro repetições de 50 sementes a 20ºC; c) quatro repetições de 25 sementes a 25ºC; d) quatro repetições de 50 sementes a 25ºC. A leitura da CE foi realizada por condutivímetro e os resultados expressos em µS.cm-1.g-1. O tamanho da semente afetou os resultados da CE; o tempo de embebição pode ser reduzido para até 3 horas; a temperatura de embebição de 25ºC mostrou-se mais promissora do que 20ºC, assim como a utilização de 50 sementes puras.

Gráficos adaptativos de controle por atributos e seu projeto na prática

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Gráfico de controle de VMAX para o monitoramento da matriz de covariâncias

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

O efeito da autocorrelação no planejamento das cartas de controle de x̄ e EWMA

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Adaptive control charts for attributes

We develop a general model for adaptive c, np, u and p control charts in which one, two or three design parameters (sample size, sampling interval and control limit width) switch between two values, according to the most recent process information. For a given in-control average sampling rate and a given false alarm rate, the adaptive chart detects changes in the process much faster than a chart with fixed parameters. Moreover, this study also offers general guidance on how to choose an effective design.

Gráfico de Hotelling com esquemas especiais de amostragem para o monitoramento de processos bivariados autocorrelacionados

Pós-graduação em Engenharia Mecânica - FEG

A Single Statistic for Monitoring the Covariance Matrix of Bivariate Processes

In this article, we present a new control chart for monitoring the covariance matrix in a bivariate process. In this method, n observations of the two variables were considered as if they came from a single variable (as a sample of 2n observations), and a sample variance was calculated. This statistic was used to build a new control chart specifically as a VMIX chart. The performance of the new control chart was compared with its main competitors: the generalized sampled variance chart, the likelihood ratio test, Nagao's test, probability integral transformation (v(t)), and the recently proposed VMAX chart. Among these statistics, only the VMAX chart was competitive with the VMIX chart. For shifts in both variances, the VMIX chart outperformed VMAX; however, VMAX showed better performance for large shifts (higher than 10%) in one variance.