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multivariate-processes
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Detection and classification mean-shifts in multi-attribute processes by artificial neural networks
, Article International Journal of Production Research ; Volume 46, Issue 11 , 2008 , Pages 2945-2963 ; 00207543 (ISSN) ; Abbasi, B ; Sharif University of Technology
2008
Abstract
To monitor the quality of a multi-attribute process, some issues arise. One of them being the occurrence of a high number of false alarms (type I error) and the other an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, based upon the artificial neural network capabilities we develop a new methodology to overcome this problem. We design a perceptron neural network to monitor either the proportions of several types of product nonconformities (instead of using several np charts) or the number of different types of defects (instead of using several c charts) in a product. Moreover, while the...
Using Multivariate Tukey distribution in Multivariare Process Capability Indices
, M.Sc. Thesis Sharif University of Technology ; Mahlooji, Hashem (Supervisor)
Abstract
In this thesis we will provide a comprehensive study of process capability indices, and we use multivariate g-and-h Tukey distribution for calculating process capability indices of multivariate non-normal processes. Univariate process capability indices have been used commonly in literature for measuring the capability of a process. However, there are several processes with more than one quality characteristic. When these characteristics are independent, we can use univariate process capability indices, but when the characteristics are dependent we should use multivariate methods for measuring process capability. There have been several studies about multivariate process capability indices,...
Process capability analysis in multivariate environment
, Article IIE Annual Conference and Expo, 5 June 2010 through 9 June 2010 ; 2010 ; Noorossana, R ; Abbasi, B ; Arena; Boeing; Colorado Technical University; et al.; FedEx Ground; The Hershey Company ; Sharif University of Technology
Institute of Industrial Engineers
Abstract
Different multivariate process capability indices are developed by researchers to evaluate process capability when vectors of quality characteristics are considered in a study. This paper presents two indices referred to as NCpM,and MCpM, in order to evaluate process capability in multivariate environment. The performance of the proposed indices is investigated numerically. Simulation results indicate that the proposed indices have descended estimation error and improved performance compared to the existing ones. These results can be important to researchers and practitioners who are interested in evaluating process capability in multivariate domain
Estimating process capability indices of multivariate nonnormal processes
, Article International Journal of Advanced Manufacturing Technology ; Volume 50, Issue 5-8 , 2010 , Pages 823-830 ; 02683768 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
Abstract
The capability analysis of production processes where there are more than one correlated quality variables is a complicated task. The problem becomes even more difficult when these variables exhibit nonnormal characteristics. In this paper, a new methodology is proposed to estimate process capability indices (PCIs) of multivariate nonnormal processes. In the proposed methodology, the skewness of the marginal probability distributions of the variables is first diminished by a root transformation technique. Then, a Monte Carlo simulation method is employed to estimate the process proportion of nonconformities (PNC). Next, the relationship between PNC and PCI is found, and finally, PCI is...
A hybrid root transformation and decision on belief approach to monitor multiattribute Poisson processes
, Article International Journal of Advanced Manufacturing Technology ; Volume 75, Issue 9-12 , December , 2014 , Pages 1651-1660 ; ISSN: 02683768 ; Javadi, S ; Fallahnezhad, M. S ; Sharif University of Technology
Abstract
Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute...
Multivariate variability monitoring using EWMA control charts based on squared deviation of observations from target
, Article Quality and Reliability Engineering International ; Volume 27, Issue 8 , 2011 , Pages 1069-1086 ; 07488017 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
Abstract
Recent research works have shown that control statistics based on squared deviation of observations from target have the ability to monitor variability in both univariate and multivariate processes. In the current research, the properties of the control statistic S t that has been proposed by Huwang et al. (J. Quality Technology 2007; 39:258-278) are first reviewed and three new S t-based multivariate schemes are then presented. Extensive simulation experiments are performed to compare the performances of the proposed schemes with those of the multivariate exponentially weighted mean squared deviation (MEWMS) and the L 1-norm distance of the MEWMS deviation from its expected value (MEWMSL 1)...
New control charts for monitoring covariance matrix with individual observations
, Article Quality and Reliability Engineering International ; Volume 25, Issue 7 , 2009 , Pages 821-838 ; 07488017 (ISSN) ; Akhavan Niaki, T ; Sharif University of Technology
2009
Abstract
It has recently been shown that the performance of multivariate exponentially weighted mean square and multivariate exponentially weighted moving variance charts of Huwang et al. (J. Qual. Technol. 2007; 39:258-278) in monitoring the variability of a multivariate process for individual observations is better than existing schemes. Both of these control charts monitor a distinct matrix which is an estimator of the in-control covariance matrix. Instead of using the trace, in this paper, we propose a L1-norm and a L2-norm-based distance between diagonal elements of the estimators from their expected values to design new control charts in monitoring the covariance matrix of a multivariate...
A transformation-based multivariate chart to monitor process dispersion
, Article International Journal of Advanced Manufacturing Technology ; Volume 44, Issue 7-8 , 2009 , Pages 748-756 ; 02683768 (ISSN) ; Akhavan Niaki, T ; Abdollahian, M ; Hosseinifard, Z ; Sharif University of Technology
2009
Abstract
Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then...