Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Multivariate control charts based on Bayesian state space models

This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA control chart to the logarithm of the Bayes' factors of the predictive error density versus the target error density. The resulting chart is proposed as capable to deal with both the non-normality and the autocorrelation structure of the log Bayes' factors. The new control charting scheme is general in application and it has the advantage to control simultaneously not only the process mean vector and the dispersion covariance matrix, but also the entire target distribution of the process. Two examples of London metal exchange data and of production time series data illustrate the capabilities of the new control chart.Comment: 19 pages, 6 figure

Three phase boost rectifier design

An electric power can be converted from one form to another form by using power electronics devices. The function of power electronics circuits by using semiconductor devices as switch is modifying or controlling a voltage. The goal of power electronics circuits are to convert electrical energy from one form to another, from source to load with highest efficiency, high availability and high reliability with the lowest cost, smallest size and weight. The term rectification refers to the power circuit whose function is to alter the ac characteristic of the line electric power to produce a “rectified”ac power at the load side that contain the dc value In this project, a study has done for the two types of rectifier topology of alternating current to direct current voltage of a three-phase boost rectifier with pulse width modulation (PWM) and a threephase boost rectifier with active power filter (APF). Power factor, shape distortion and voltage can be increased as much as seen through two types of this topology if it is connected to the non-linear loads in power systems. Three phase rectifier with pulsewidth modulation (PWM) is one of controlled rectifier consist six pulses divides into two groups which are top group and bottom group. For top group, IGBT with its collector at the highest potential will conduct at one time. The other two will be reversed. Thus for bottom group, IGBT with the its emitter at the lowest potential will conduct. This project also observes the current, voltage waveform and the harmonics component when the active power filter (AFC) placed in series with non-linear load. Type of rectifier used is uncontrolled rectifier. In this work MATLAB/SIMULINK power system toolbox is used to simulate the system Results of simulations carried out, the advantages and disadvantages, the increase in voltage and waveform distortion for the system under consideration can be show

Reflective jacket

Safety product is created for workers, students, people and society to prevent from dangerous, harmful, injured also risks situation that can be occurs before, during and after works. The materials to produce the safety product must be strong, hard, good resistance, fast treatment and others characteristic that can be protect and prevent the user from the dangerous. Sometimes, the cost to produce the safety product is expensive because of the material to make the safety product become the good products

Statistical Process Control Methods for Individual Observations

International audienceStatistical process control methods for monitoring processes with individual measurements are considered and two new individual control charts for monitoring process variability and correlation are proposed. The influence function of variance is proposed to monitor process variability. To investigate correlation among two quality characteristics control charts based on the influence function of correlation coefficient are suggested. The advantage of our variance influential control chart is its ability to monitor process variance based only on the measurements of each inspected unit, which is not the case for classical moving range chart where differences from one point to the next are displayed in the graphic, so limiting its use in the matter of mated parts. The proposed techniques are general, and the influence functions may be used to build up individual control charts relative to either nominal values or estimates. The method is further illustrated with real datasets, from a manufacturing system producing precisely interfitting and mating parts

Efficient and Highly Robust Hotelling T² Control Charts Using Reweighted Mininum Vector Variance

Hotelling T² control chart is an effective tool in statistical process control for multivariate environment. However, the performance of traditional Hotelling T² control chart using classical location and scatter estimators is usually marred by the masking and swamping effects. In order to alleviate the problem, robust estimators are recommended. The most popular and widely used robust estimator in the Hotelling T² control chart is the minimum covariance determinant (MCD). Recently, a new robust estimator known as minimum vector variance (MVV) was introduced. This estimator possesses high breakdown point, affine equivariance and is superior in terms of computational efficiency. Due to these nice properties, this study proposed to replace the classical estimators with the MVV location and scatter estimators in the construction of Hotelling T² control chart for individual observations in Phase II analysis. Nevertheless, some drawbacks such as inconsistency under normal distribution, biased for small sample size and low efficiency under high breakdown point were discovered. To improve the MVV estimators in terms of consistency and unbiasedness, the MVV scatter estimator was multiplied by consistency and correction factors respectively. To maintain the high breakdown point while having high statistical efficiency, a reweighted version of MVV estimator (RMVV) was proposed. Subsequently, the RMVV estimators were applied in the construction of Hotelling T² control chart. The new robust Hotelling T² chart produced positive impact in detecting outliers while simultaneously controlling false alarm rates. Apart from analysis of simulated data, analysis of real data also found that the new robust Hotelling T² chart was able to detect out of control observations better than the other charts investigated in this study. Based on the good performance on both simulated and real data analysis, the new robust Hotelling T² chart is a good alternative to the existing Hotelling T² charts

On Data Depth and the Application of Nonparametric Multivariate Statistical Process Control Charts

The purpose of this article is to summarize recent research results for constructing nonparametric multivariate control charts with main focus on data depth based control charts. Data depth provides data reduction to large-variable problems in a completely nonparametric way. Several depth measures including Tukey depth are shown to be particularly effective for purposes of statistical process control in case that the data deviates normality assumption. For detecting slow or moderate shifts in the process target mean, the multivariate version of the EWMA is generally robust to non-normal data, so that nonparametric alternatives may be less often required