Sharif Digital Repository

In this paper, a new multi-attribute T2 control chart is initially proposed to monitor multi-attribute processes based on a transformation technique. Then, the maximum likelihood estimator of a multivariate Poisson process change point is derived for unknown changes that are assumed to belong to a family of monotonic changes. Using extensive simulation experiments, the performance of the proposed change-point estimator is compared to the ones derived for step changes and linear-trend disturbances, when the true change types are step change, linear trends and multiple-step changes. We show when the type of the change is not known a priori, the proposed estimator is an appropriate choice,... 

Because of the importance of health-care processes to people life, researchers attempted to reduce death rates using risk-adjusted control charts. In this paper, the number of patients survived at least 30days after a surgery is monitored using a novel risk-adjusted geometric control chart. In this chart, the patient risk is modeled using a logistic regression. The new scheme is proposed to be used in Phase-I where a likelihood ratio test derived from a change-point model is employed. The application of the proposed chart is demonstrated in a case study. Furthermore, through simulation studies, it is shown that the proposed control chart is more effective in terms of power than the chart... 

Control charts are useful tools of monitoring quality characteristics. One of the problems of employing a control chart is that the time it alarms is not synchronic with the time when assignable cause manifests itself in the process. This makes difficult to search and find assignable causes. Knowing the real time of manifestation of assignable cause (change point) helps to find assignable cause(s) sooner and eases corrective actions to be taken. In this paper, a probabilistic neural network (PNN)-based procedure was developed to estimate the variance change point of a normally distributed quality characteristic. The PNN was selected based on trial and error among different types of... 

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... 

Purpose: The purpose of this paper is to propose two control charts to monitor multi-attribute processes and then a maximum likelihood estimator for the change point of the parameter vector (process fraction non-conforming) of multivariate binomial processes. Design/methodology/approach: The performance of the proposed estimator is evaluated for both control charts using some simulation experiments. At the end, the applicability of the proposed method is illustrated using a real case. Findings: The proposed estimator provides accurate and useful estimation of the change point for almost all of the shift magnitudes, regardless of the process dimension. Moreover, based on the results obtained... 

In this paper, we first propose a maximum likelihood estimator (MLE) of a change point in high-yield processes, where the only assumption is that the change belongs to a family of monotonic changes. Following a signal from the cumulative count of conforming (CCC) control chart, the performance of the proposed monotonic change-point estimator is next evaluated by comparing its performances to the ones designed for step-changes and linear-trend disturbances through extensive simulation experiments involving different single step-changes, linear-trend disturbances, and multiple-step changes. The results show that when the type of change is not known a priori, using the proposed change-point... 

In this paper, the maximum likelihood estimator (MLE) of the change point in a high-yield process when a linear trend disturbance occurs in the proportion nonconformity of the process is first derived. Then, the performances of the proposed change point estimator in terms of both accuracy and precision are compared to the MLE of the change point designed for step changes. The results of the comparison analysis that is performed using Monte Carlo simulation experiments show that not only the average estimates of the change point estimator designed for linear trends are closer to the real change point, but also its mean square error is smaller than the one of the estimator designed for step... 

When a control chart signals an out-of-control condition, knowing when the process has really changed (the change point) accelerates the identification of the source of special causes and makes the corrective measures to be taken sooner. In this paper, a new multi-attribute T 2 control chart based on two transformation methods is initially proposed to monitor the parameter vector of multi-attribute Poisson processes. Then, the maximum likelihood estimators (MLE) of the process change point designed for both linear trend and step change disturbances are derived. Next, using Monte Carlo simulation, we show the performances of the proposed estimators are satisfactory. Finally, through... 

In this paper, maximum likelihood step-change point estimators of the location parameter, the out-of-control sample and the out-of-control stage are developed for auto-correlated multistage processes. To do this, the multistage process and the concept of change detection are first discussed. Then, a time-series model of the process is presented. Assuming step changes in the location parameter of the process, next, the likelihood functions of different samples before and after receiving out-of-control signal from an X-bar control chart were derived under different conditions. The maximum likelihood estimators were then obtained by maximizing the likelihood functions. Finally, the accuracy and... 

In multi-attribute process monitoring, when a control chart signals an out-of-control condition indicating the existence of a special cause, knowing when the process has really changed (the change point) accelerates the identification of the source of the special cause and makes the corrective measures to be employed sooner. This, of course, results in a considerable amount of savings in time and money. Since many real world multi-attribute processes are Poisson and most process changes are step-change, a new method is proposed, in this paper, to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multivariate Poisson processes. In this method, two... 

knowing the time of a process change would simplify the search, identification, and removal of the special causes that disturbed the process. Since, in many real world manufacturing systems, the production of goods comprises several autocorrelated stages; in this paper, the problem of the change point estimation for such processes is addressed. A first order autoregressive model (AR(1)) is used to model a multistage process observations, where a X -chart is established for monitoring its mean. A step change is assumed for the location parameter of the model. After receiving an out-of-control signal, in order to determine the stage and the sample that caused the change (hence finding the time... 

Identification of the change point in multi-attribute processes make the corrective measures to be employed sooner. This of course results in considerable amount of time and money savings. In real-world problems, since most of the process changes are due to instantaneous causes (step-change), a new method is proposed in this paper to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multi-attribute processes. The results of a performance study based on a numerical example are encouraging  

Despite the fact that control charts are able to trigger a signal when a process has changed, it does not indicate when the process change has begun. The time difference between the changing point and a signal of a control chart could cause confusions on the sources of the problems. Knowing the exact time of a process change would help to reduce the time for identification of the special cause. In this article, a model for the change-point problem is first introduced and a maximum-likelihood estimator (MLE) is applied when a linear trend disturbance is present. Then, Monte Carlo simulation is applied in order to evaluate the accuracy and the precision performances of the proposed... 

Usually the time a control chart shows an out-of-control signal is not the exact time at which a change happens; instead, the change has started before this time. The exact time the change starts is called the change point. Although many manufacturing processes are of a multistage type, most of change point estimations in the literature focused on processes with a single stage. In this research, a multistage process with a single quality characteristic monitored in each stage is first modeled using both a first-order autoregressive (AR(1)) and an autoregressive moving average (ARMA(1, 1)) model. Then, a maximum likelihood estimator is derived to estimate the change points, i.e., the sample... 

The difference between the signaling time and the real change point of a process is an important monitoring issue. If the exact time at which the change manifests itself into the process is known, then process engineers can identify and eliminate the root causes of process disturbance efficiently and quickly, resulting in considerable amount of time and cost savings. Multistage count processes that are often observed in production environments must be monitored to assure quality products. In this study, multistage Poisson count processes are first introduced. Then, the process is modeled using a first-order integer-valued autoregressive time series (INAR(1)). For out-of-control signals...