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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... 

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... 

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,... 

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... 

This effort attempts to study the change point problem in the area of non-linear profiles. A method based on Artificial Neural Networks (ANN) is proposed for estimating the real time of a single step change. The feature vector of the proposed Multi-Layer Perceptron (MLP) is based on Z and control chart statistics for nonlinear profiles. The merits of the proposed estimator are evaluated through simulation experiments. The results show that the estimator provides an accurate estimate of the single step change point in non-linear profiles in the selected case problem. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of... 

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... 

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... 

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... 

The Phase I applications of the statistical profile monitoring have recently been extended to the case when the response variable is binary. We are motivated to undertake the current research in an attempt to try to provide a unified framework for the Phase I control in the context of statistical profile monitoring that can be used to tackle a large class of response variables, such as continuous, count, or categorical response variables. The unified framework is essentially based on applying the change point model to the class of generalized linear models. The proposed Phase I control chart is assessed and compared with the existing charts under binomial and Poisson profiles. Some... 

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 30 days 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... 

Monitoring surgical processes has gained prominence by accounting for patients’ health condition prior to surgery in recent years. However, most of previous researchers have focused on Phase-II monitoring based on binary outcomes, while very little attention has been paid to Phase-I monitoring procedures, especially when the outcomes are continuous. In this paper, a general Phase-I accelerated failure time-based risk-adjusted control chart is proposed to monitor continuous surgical outcomes based on a likelihood-ratio test derived from a change-point model. Different from the existing models, this paper shows that continuous outcomes depend not only on the patient conditions described by the... 

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... 

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...