Search for: multivariate-quality-control
Article Quality and Reliability Engineering International ; Volume 21, Issue 8 , 2005 , Pages 825-840 ; 07488017 (ISSN) ; Abbasi, B ; Sharif University of Technology
Most multivariate quality control procedures evaluate the in-control or out-of-control condition based upon an overall statistic, like Hotelling's T2. Although T2 is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out-of-control conditions and to help identify aberrant variables when Shewhart-type multivariate control charts based on Hotelling's T2 are used. The results...
M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Mohammad Taghi
Control charts are the most important tools of statistical quality control. The problem that exists is that control charts do not show the real time the shift in a process started. The real time a change occurs in a process is called the change point. In this research, the monotonic change point in a multivariate normal process is estimated using the maximum likelihood estimation approach, where the process is monitored by a multivariate exponentially weighted moving average scheme. Mote Carlo simulation studies are performed to evaluate the performance of the proposed approach
M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi
In this research, we consider the application of copulas in multivariate quality control problems. In particular, we consider two specific problems. The first problem concerns the situation where the normality assumption is rejected. In this case, copulas can be used as a flexible tool to define a broad range of multivariate distributions with different dependence structure as well as marginal distributions. The second problem concerns proposing control charts to monitor the dependence structure among quality characteristics. The proposed method not only produces an out-of-control signal when dependence structure among variables deviates from the specified one, but also can be used to...
Article 35th International Conference on Computers and Industrial Engineering, ICC and IE 2005, Istanbul, 19 June 2005 through 22 June 2005 ; 2005 , Pages 1-6 ; 9755612653 (ISBN); 9789755612652 (ISBN) ; Akhavan Niaki, S. T ; Arkat, J ; Sharif University of Technology
Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and process variable space are considered in this paper. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, we assume that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of...
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
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...
A heuristic threshold policy for fault detection and diagnosis in multivariate statistical quality control environments, Article International Journal of Advanced Manufacturing Technology ; Volume 67, Issue 5-8 , July , 2013 , Pages 1231-1243 ; 02683768 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the...
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
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...
A new statistical process control method to monitor and diagnose bivariate normal mean vectors and covariance matrices simultaneously, Article International Journal of Advanced Manufacturing Technology ; Volume 43, Issue 9-10 , 2009 , Pages 964-981 ; 02683768 (ISSN) ; Ostadsharif Memar, A ; Sharif University of Technology
In this paper, in order to find an adequate method of monitoring the mean vector and covariance matrix of a production process simultaneously, first, some available univariate control methods were reviewed and evaluated. Then, the maximum exponentially weighted moving average method with a better potential application and good performances in terms of average time to signal (ATS) criterion was selected to be extended to the bivariate case. In the extended procedure, by proper transformation of the control parameters, the primary control space is transformed such that all control elements have the same probability distributions. In this case, only the maximum absolute value of the transformed...
Article International Journal of Engineering, Transactions A: Basics ; Volume 20, Issue 3 , 2007 , Pages 233-242 ; 17281431 (ISSN) ; Abbasi, B ; Arkat, J ; Sharif University of Technology
Materials and Energy Research Center 2007
Statistical process control methods for monitoring processes with univariate or multivariate measurements are used widely when the quality variables fit to known probability distributions. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, it is assumed that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of...
Article International Journal of Production Research ; Volume 47, Issue 1 , 2009 , Pages 251-271 ; 00207543 (ISSN) ; Davoodi, M ; Sharif University of Technology
In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate-multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate-multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average...