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Recently, many researchers and practitioners have shown interest on profile monitoring as a relatively new subarea of statistical process control. One main reason for this interest, and perhaps a key factor for the contributions of many researchers to this field, is the various applications of profile monitoring in real life. Although one can easily encounter many univariate applications of profile monitoring in service and manufacturing environments, there exist situations where quality of a product or process needs to be modeled in multivariate terms. In this paper, we investigate monitoring of multivariate simple linear profiles in phase II when independence assumption of observations... 

Knowing the time of change would narrow the search to find and identify the variables disturbing a process. The knowledge of the change point can greatly aid practitioners in detecting and removing the special cause(s). Count processes with an autocorrelation structure are commonly observed in real-world applications and can often be modeled by the first-order integer-valued autoregressive (INAR) model. The most widely used marginal distribution for count processes is Poisson. In this study, change-point estimators are proposed for the parameters of correlated Poisson count processes. To do this, Newton's method is first used to approximate the parameters of the process. Then, maximum... 

Network monitoring is a new area in statistical process control applications. It aims at detecting assignable changes in the communication structure of a network. The probability of communications in social networks is usually based on the attributes of vertices. Moreover, due to the nature of human relationships, social networks are almost time-dependent. Neglecting this feature in control chart design reduces the chart performance. In this paper, communications are defined as autoregressive binary variables with the probability modeled by the logit link function. The explanatory variables of the model are the vertices’ attributes and previous information of the network. Accordingly, we... 

The quality characteristics of both manufacturing and service industries include not only the variables but the attributes as well. While a substantial research have been performed on auto-correlated variables, little attempt has been fulfilled for auto-correlated attributes. Ignoring the imbedded autocorrelation structure in constructing control charts cause not only the in-control run length to decrease, but also the false alarms to increase. To overcome these shortcomings, in this research, an autoregressive vector first models the autocorrelation structure of the process data. Then, a modified Elman neural network is developed to generate simulated data using the ARTA algorithm. Next, a... 

Quality characteristics are subject of both manufacturing and service industries, which include not only the variables but the attributes as well. In Quality Control area substantial research has been done for Auto-correlated variables; however, no attempt was done for Auto-correlated attributes. Ignoring the autocorrelation structure in constructing control charts cause the in-control run length to decrease, and the false alarms to increase as such. In this article we develop a new methodology based upon the modified Elman neural network capabilities to overcome this problem. Moreover, instead of back propagation, simulated annealing is suggested as an alternative training technique that is...