Sharif Digital Repository

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, no 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 auto-regressive (AR) 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,... 

Profile Monitoring is a new research topics in the field of statistical process control. In many applications of statistical quality control, sometimes quality of a process or product described by a relationship between a response variable and one or more independent variables.The researchers call this relationship profile. The study seeks to demonstrate that monitoring profiles In addition, the industry can be effective in medical field and improves control of the patient's condition by a doctor. Profiles are usually modeled with a linear regression equation, non-linear or polynomial. finally, the results of this study, using actual data, models and methods to predict and control... 

Control charts are one of the most important tools in statistical process control to detect assignable causes. The goal of a control chart is to detect an out-of-control state quickly so that process engineers can initiate their search for the special cause sooner. Once the special cause has been identified, the appropriate action can then be taken to improve the process.A new method to estimate the change point of Poisson rate parameter when the step change occurs is proposed in this thesis. That is, the rate parameter is assumed to suddenly shift from its in-control value to an out-of control value at a single unknown point in the process.To do this, a belief that the process is in-control... 

There are many cases in industrial and non-industrial sections where the quality characteristics are in the form of profiles. A profile is the functional relationship between a response variable and one or more predictor variables used to describe the quality of a process. Profile monitoring is the implementation of statistical process control techniques for this purpose. According to the type of relationship between response variable and predictor variables, profiles are classified into many categories such as: simple linear profiles, multiple linear profiles, nonlinear profiles and generalized linear profiles. Most of the research efforts in the area of profile monitoring have been... 

Monitoring a surgical process has gained prominence by accounting for patiens̛ health condition prior to surgery in recent years. How even most existing researches focus on phase 2 monitoring and binary outcomes. Very little work has been done on phase 1 control of outcomes and no one addressing the issue of phase 1 monitoring of continuous outcomes. Risk adjustment is accomplished using accelerated failure time (AFT) regression models. In this thesis a general phase 1 risk-adjusted control chart is proposed for monitoring continuous surgical outcomes based on a likelihood-ratio test derived from a change-point model. Different from the existing models, this thesis shows that continuous... 

Control chart is one of the most powerful tools in statistical process control (SPC). This tool has been widely used in industrial schemes; however, in recent years, it has become widespread in health care studies as well. Most of these studies are dealing with monitoring qualitative variables in one-stage processes. This research presents a new mechanism for monitoring medical processes, by modelling the bone marrow transplant surgery as a dependent multi-stage process into a multiple profile. In this mechanism, the characteristics which will be monitored are quantitative, and the cascade property between stages is considered. Moreover, risk adjustment methods have been used due to the... 

Control charts have always had an undeniable role in Statistical Control of processes in many fields. The growth of quality characteristics to be monitored, has led to the vast utilization of multivariate control charts. These variables are characterized by relatively high correlation between them. The complicated structure of measured variables has lessened the reliability of conventional control charts. Projection methods have been developed to address the problem of high correlated variables by transforming the correlated variables to an uncorrelated set of variables. Among them, Principal Component Analysis based control charts have been widely used to overcome the problem of correlated... 

Control charts are one of the strongest optimization tools. Control charts issue warning due to out of control processby recorded data. As soon as charts warn, attempt start to find the changes reason.Finding out the issue on time save a lot of time and cost. By determining the time of this change, finding out the problem reason get faster. The real time of process change called change point. There exist many papers in change point field finding real time of change in literature. Each one considers the problem with specific assumptions. These assumptions consist of distribution function, change type, parameters and solution procedure. One kind of existing papers are about to determine normal... 

Control charts are the best tools for determining the deviations in the main parameters of a process. Exponentially weighted moving average, EWMA, control charts are the best type for determining small deviations. Determining the control charts parameters by means of minimizing a cost model is economic design. Economic-statistical design is achieved by adding statistical constraints to the economic model. Average run length when the process is in control, ARL0, and average run length when the process is out of control, ARL1, are limited in the economic-statistical model. In this thesis the economic-statistical design of a multivariate EWMA control chart is considered and this model is solved... 

We consider finding some efficient control schemes for multivariate variability monitoring with capability of working with individual observations. To do this, the existing efficient control charts for multivariate variability monitoring are studied first and it is determined that the and control statistics, defined by individual observations, estimate the covariance matrix quite well. However, the control method that is based on monitoring the trace of these matrices is not necessarily the best. Thus, by applying the first and the second norm on these two statistics, four new control schemes, namely MEWMSL1, MEWMVL1, MEWMVL1 and MEWMVL2are proposed. Performance comparison results show... 

Today we live in an era of continuous technology improvement which results in huge changes in different areas of diverse industries. Among the most recent systems for monitoring and quality control which benefits from high speed, are machine vision systems. The output of these systems, are digital images that can be used for monitoring instead of the original products. Unfortunately due to the computational complexity of data extracted from the digital images, traditional methods lose their efficiency. Therefore, in this thesis, a method is proposed to design a model for the monitoring and control of image-based processes, which uses classification methods, that are capable of classifying... 

The aim of this research is to study performance of Rao Score Test control chart in phase II monitoring of generalized linear profiles for drift and multiple changes which can be isotonic or antitonic. Moreover, the performance of the method is compared with two common methods in the generalized linear profile literature: Hotelling T2 and multivariate exponential weighted moving average. Afterward multivariate cumulative sum chart is proposed to be used in monitoring antitonic multiple change in the parameter of Poisson profiles. Finally, a real world example is presented in which Rao Score Test method is applied to real data and the performance of this method is compared with other methods.... 

In a production process, when the quality of a product depends on more than one characteristic, and there is correlation between them, using univariate control charts increases type І and type ΙΙ errors. So for monitoring these processes, multivariate quality control charts are used. Multivariate statistical process control is receiving increased attention in the literature,but little work has been done to deal with multi-attribute processes and just in recent years some techniques are developed in this field. In this thesis, based on the power transformation concept, two new techniques have been developed to monitor multi-attribute processes, in which the defect counts are important. In the... 

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...