Control Charts With Estimated Process Parameters And A Proposed Coefficient Of Variation Chart

Carta kawalan menerima perhatian besar dalam Kawalan Proses Berstatistik (SPC) sebagai alat yang paling berguna dalam pengesanan anjakan proses supaya tindakan pembetulan boleh diambil untuk mengenal pasti dan menyingkirkan sebab-sebab terumpukkan yang hadir. Objektif utama penyelidikan ini adalah untuk menangani masalah anggaran parameter proses bagi carta larian kumpulan dengan kepekaan sisi (carta SSGR) dan carta pensampelan ganda dua sintetik (carta SDS). Kebanyakan carta kawalan memerlukan andaian bahawa nilai sasaran parameter proses dalam kawalan, iaitu min dan sisihan piawai adalah diketahui untuk pengiraan had-had kawalan serta statistik carta kawalan. Malangnya, parameter proses lazimnya tidak diketahui dalam keadaan sebenar, yang mana nilainya dianggarkan daripada set data Fasa-I dalam kawalan. Dalam penyelidikan ini, prestasi carta-carta SSGR dan SDS dengan parameter proses yang dianggarkan dibandingkan dengan prestasi carta-carta yang sepadan apabila parameter proses diketahui. Control charts receive great attention in Statistical Process Control (SPC) as the most useful tool in detecting process shifts so that corrective actions can be taken to identify and eliminate the assignable causes that are present. The main objective of this research is to address the problems of estimation of process parameters for the side sensitive group runs (SSGR) chart and synthetic double sampling (SDS) chart. Most control charts require the assumption that the target values of the in-control process parameters, i.e. the mean and standard deviation are known for the computation of the control charts’ control limits and statistics. Unfortunately, process parameters are usually unknown in real situations, where they are estimated from an in-control Phase-I dataset. In this research, the performances of the SSGR and SDS charts with estimated process parameters are compared with that of their known process parameters counterparts

New Variable Sampling Interval Run Sum Standard Deviation And Run Sum Multivariate Coefficient Of Variation Charts

In Statistical Process Control (SPC), the control charting technique is an effective method to solve quality issues in manufacturing and service industries. The R and S charts are commonly used to monitor the process variance in industries due to the charts’ simplicity and high sensitivity toward large shifts. However, these charts are not sensitive toward small and moderate shifts in the process variance. On the other hand, the more sophisticated charts, such as the exponentially weighted moving average (EWMA) S chart and the cumulative sum (CUSUM) S chart are very effective in detecting small changes in the process variance. However, most quality practitioners do not adopt these charts in real applications due to their design complexity. In view of this setback, the variable sampling interval (VSI) approach is incorporated into the run sum (RS) S chart, in order to suggest an effective, yet a simple chart, for detecting small, moderate and large shifts in the process variance. Apart from that, the coefficient of variation (CV) is an important quality characteristic to take into account when the process mean and standard deviation are not constant, even though the process is in-control

Extensions Of Multivariate Coefficient Of Variation Control Charts

Control charts for monitoring multivariate coefficient of variation (MCV) are applied when the interest is in monitoring the relative multivariate variability to the mean vector of a multivariate process. This thesis proposes an upper-sided variable sampling interval (VSI) exponentially weighted moving average (EWMA) chart to detect upward shifts in the MCV squared

The Revised M-Of -K Runs Rules Based On Median Run Length

Petua larian digunakan untuk meningkatkan kepekaan carta kawalan X Shewhart dalam pengesanan anjakan min proses yang kecil dan sederhana. Runs rules are used to increase the sensitivity of the Shewhart X control chart in detecting small and moderate process mean shifts

Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes

This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. 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