• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 10
  • 6
  • 1
  • Tagged with
  • 20
  • 20
  • 20
  • 20
  • 20
  • 10
  • 5
  • 5
  • 5
  • 5
  • 5
  • 4
  • 4
  • 3
  • 3
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Economic design of control charts for multivariate, multistate processes

Harris, Richard John 08 1900 (has links)
No description available.
2

Economic design of control charts for correlated, multivariate observations

Alt, Francis Bernard 08 1900 (has links)
No description available.
3

A COMPARISON OF TWO MULTIVARIATE CUMULATIVE SUM CONTROL CHART TECHNIQUES.

Korpela, Kathryn Schuler, 1960- January 1986 (has links)
No description available.
4

A VARIABLE SAMPLING FREQUENCY CUMULATIVE SUM CONTROL CHART SCHEME

Myslicki, Stefan Leopold, 1953- January 1987 (has links)
This study uses Monte Carlo simulation to examine the performance of a variable frequency sampling cumulative sum control chart scheme for controlling the mean of a normal process. The study compares the performance of the method with that of a standard fixed interval sampling cumulative sum control chart scheme. The results indicate that the variable frequency sampling cumulative sum control chart scheme is superior to the standard cumulative sum control chart scheme in detecting a small to moderate shift in the process mean.
5

Economically optimal control charts for two stage sampling

Hall, Kathryn B. 23 January 1990 (has links)
Control charts are designed to monitor population parameters. Selection of a control chart sampling plan involves determination of the frequency of samples, size of each sample, and critical values to determine when the system is sending an out-of-control signal. Since the main use of control charts is in industry, a widely accepted measure of a good sampling plan is one that minimizes the total cost of operating the system per unit time. Methods for selection of control chart sampling plans for economically optimal X charts are well established. These plans focus on single stage sampling at each sampling period. However, some populations naturally call for two stage sampling. Here, the cost of operating a system per unit time is redefined in terms of two stage sampling plans, and computer search techniques are developed to determine the control chart parameters. First the sample sizes and critical values are fixed, and Newton's method is used to determine the optimal time between samples. Then, a Hooke - Jeeves search is used to simultaneously determine the optimal critical value, sample sizes and time between samples. Adjustment to the latter is required whenever any of the other three parameters change. Alternative methods are also discussed. Information from a single sample is usually used to control shifts in both the process mean and variance. With two stage sampling, this means two additional control charts are used, one for each variance component. The computer algorithm developed for selection of parameters for X charts is adapted by expanding the Hooke Jeeves search region to a six dimensional space, now over three critical values, sample sizes for both stages of sampling, and the time between samples. These methods are applied to a real data set that requires two stage sampling. A representative analysis of the sensitivity of the optimal sampling scheme to the input parameters completes the paper. / Graduation date: 1990
6

Application of discrete distributions in quality control

Scheffler, Milton Richard 12 1900 (has links)
No description available.
7

Control chart procedures based on cumulative gauging scores

Chung, Jain January 1985 (has links)
Control charts based on cumulative gauging scores rely on gauge scoring systems used for transforming actual observations into integer gauging scores. In some cases, the gauging scores are easy to obtain by using a mechanical device such as in the go-no-go inspection process. Thus, accurate measurements of selected quality characteristics are not necessary. Also, different control purposes can be achieved p by using different scoring systems. Cumulative gauging score charts based on two pairs of gauges are proposed to control the process mean or the standard deviation by either gauging one or several observations. Both random walk and cusum type cumulative gauging score charts are used. For controlling the process mean and standard deviation at the same time, a cusum type and a two-dimensional random walk type procedure are proposed. A gauging scheme can be applied to multivariate quality control by gauging either x² or T² statistics. A simple multivariate control chart which is based on the multivariate sign score vector is also proposed. The exact run length distribution of these cumulative gauging score charts can be obtained by formulating the procedures as Markov chain processes. For some procedures, the average run length (ARL) can be obtained in a closed form expression by solving a system of difference equations with appropriate boundary conditions. Comparisons based on the ARL show that the cumulative gauging score charts can detect small shifts in the quality characteristic more quickly than the Shewhart type X-chart. The efficiency of the cusum type gauging score chart is close to the regular CUSUM chart. The random walk type gauging score chart is more robust than the Shewhart and CUSUM charts to observations which have heavy a tailed distribution or which are serially correlated. For multivariate quality control. A procedure based on gauging the x² statistic has better performance than the x² chart. Also, a new multivariate control chart procedure which is more robust to the misspecification of the correlation than the x² chart is proposed. / Ph. D.
8

Control charts based on residuals for monitoring processes with correlated observations

Lu, Chao-Wen 10 November 2005 (has links)
In statistical process control, it is usually assumed that observations on the process output at different times are lID. However, for many processes the observations are correlated and control charts for monitoring these processes have recently received much attention. For monitoring the process level, this study evaluates the properties of control charts, such as the EWMA chart and the CUSUM chart, based on the residuals from the forecast values of an ARMA model. It is assumed that the process mean is a ftrst order autoregressive (AR(l)) model and the observations are the mean plus a random error. Properties of these charts are evaluated using a Markov chain approach or an integral equation approach. The performance of control charts based on the residuals is compared to the performance of control charts based on the original observations. A combined chart using forecasts and residuals as the control statistics as well as a combined chart using the EWMA of observations and the EWMA of residuals as the control statistics are also studied by simulation. It is found that no universally "good" chart exists among all the charts investigated in this study. In addition, for monitoring the process variance, two kinds of EWMA chart based on residuals are studied and compared. / Ph. D.
9

A comparison of alternative methods to the shewhart-type control chart

Hall, Deborah A. 08 September 2012 (has links)
A control chart that simultaneously tracks the mean and variance of a normally distributed variable with no compensation effect is defined in this work. This joint control chart is compared to five other charts: an Χ chart, an s² chart, a Reynolds and Ghosh chart, a Repko process capability plot, and a t-statistic chart. The criterion for comparison is the probability of a Type II sampling error. Several out-of-control cases are examined. In the case of Repko, an equation is defined to compute the Type II error probability. The results indicate that the Reynolds and Ghosh statistic is powerful for cases when the variance shifts out of control. The Χ chart is powerful when the mean shifts with moderate changes in the variance. The joint chart is powerful for moderate changes in the mean and variance. / Master of Science
10

The application of a single control chart for dependent variables in multivariate quality control

Hanson, Robert Alexander 02 May 2009 (has links)
Most control charts monitor only one quality characteristic. There are, however, many manufactured products for which good quality requires meeting specifications in more than one physical characteristic. Typical practice when dealing with multiple quality characteristics is to take a separate sample for each characteristic and then create individual univariate control charts which are independently monitored. This method can result in errors due to not accounting for the effects of correlation. In order to avoid these errors, an alternate approach to multivariate quality control problems is proposed and studied here. The original problem is converted into a univariate problem by using the following transformation: y=Σ a<sub>i</sub>x<sub>i</sub> i where αi = weighting coefficient for the i<sup>th</sup> quality characteristic X<sub>i</sub> = represents the i<sup>th</sup> quality characteristic This transformation retains sensitivity to changes in the original quality variables. The resulting univariate quality control model takes into account the sampling error probabilities for each of several candidate hypotheses. The probabilities of correctly diagnosing process shifts when an out-of-control state occurs are calculated and tabulated as are the probabilities that the model will signal when an out-of-control state occurs. / Master of Science

Page generated in 0.1263 seconds