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    Monitoring multivariate profiles in multistage processes

    , Article Communications in Statistics: Simulation and Computation ; Volume 50, Issue 11 , 2021 , Pages 3436-3464 ; 03610918 (ISSN) Bahrami, H ; Akhavan Niaki, T ; Khedmati, M ; Sharif University of Technology
    Taylor and Francis Ltd  2021
    Abstract
    In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

    Monitoring multivariate profiles in multistage processes

    , Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) Bahrami, H ; Akhavan Niaki, T ; Khedmati, M ; Sharif University of Technology
    Taylor and Francis Inc  2019
    Abstract
    In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

    Monitoring multivariate profiles in multistage processes

    , Article Communications in Statistics: Simulation and Computation ; 2019 ; 03610918 (ISSN) Bahrami, H ; Akhavan Niaki, S. T ; Khedmati, M ; Sharif University of Technology
    Taylor and Francis Inc  2019
    Abstract
    In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple... 

    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) Nezhad, M. S. F ; Niaki, S. T. A ; Sharif University of Technology
    2013
    Abstract
    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... 

    Monitoring project duration and cost in a construction project by applying statistical quality control charts

    , Article International Journal of Project Management ; Volume 31, Issue 3 , April , 2013 , Pages 411-423 ; 02637863 (ISSN) Aliverdi, R ; Moslemi Naeni, L ; Salehipour, A ; Sharif University of Technology
    2013
    Abstract
    The earned value is a leading technique in monitoring and analyzing project performance and project progress. Although, it allows exact measurement of project progress, and can uncover any time and cost deviations from the plan, its capability in reporting accepted level of deviation is not well studied. This study presented an approach to overcome this limitation by applying statistical quality control charts to monitor earned value indices. For this purpose, project time and cost performance indices of a real construction project were monitored regularly on individual control charts. The results were quite promising, and not only competed well against traditional approaches, but also... 

    A Max-EWMA approach to monitor and diagnose faults of multivariate quality control processes

    , Article International Journal of Advanced Manufacturing Technology ; Volume 68, Issue 9-12 , 2013 , Pages 2283-2294 ; 02683768 (ISSN) Nezhad, M. S. F ; Niaki, S. T. A ; Sharif University of Technology
    2013
    Abstract
    A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods.... 

    Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms

    , Article Applied Mathematics and Computation ; Volume 218, Issue 19 , 2012 , Pages 9664-9675 ; 00963003 (ISSN) Zoraghi, N ; Abbasi, B ; Niaki, S. T. A ; Abdi, M ; Sharif University of Technology
    2012
    Abstract
    The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as... 

    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) Akhavan Niaki, T ; Ostadsharif Memar, A ; Sharif University of Technology
    2009
    Abstract
    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... 

    Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems

    , Article International Journal of Advanced Manufacturing Technology ; Volume 42, Issue 7-8 , 2009 , Pages 713-724 ; 02683768 (ISSN) Akhavan Niaki, T ; Fallah Nezhad, M. S ; Sharif University of Technology
    2009
    Abstract
    A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and... 

    Monitoring of Multivariate Profiles in Multistage Process

    , M.Sc. Thesis Sharif University of Technology Bahrami, Hassan (Author) ; Akhvan Niaki, Taghi (Supervisor)
    Abstract
    Nowadays, due to the advancement in manufacturing technology and increasing use of information technology in the services and industries, most of production proccess consist of complex and high-dimensional data. These processes include multivariate processes, complex profiles and multistage processes. In some quality control applications, processes consist of multiple components, stations or stages to finish the final product or service which are called multistage processes. In addition, some quality characteristics in each stage can be represented by a relationship between a response variable and one or more explanatory variables which is named as profile. In this research, a general model... 

    The Application of Profile Monitoring in Healthcare for Disease Monitoring- case Study

    , M.Sc. Thesis Sharif University of Technology Chabok, Sheyda (Author) ; Rafiee, Majid (Supervisor)
    Abstract
    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... 

    Application of Process Control Charts to Improvement of Survival Time of Patients with Gastric Cancer

    , M.Sc. Thesis Sharif University of Technology Rezaie Jahan, Hamid Reza (Author) ; Mahlooji, Hashem (Supervisor)
    Abstract
    Statistical process control charts have been applied to the health care practices for quite same time. This work aims at investigating the merits to of applying process control chart to the survival time of the patients suffering from gastric cancer-who go though the surgical treatment. Based on accelerated failure time regression models we adopt risk adjusted control chart to monitor the surgical outcome. The monitoring process will we performed continuously based on a likelihood ratio test. The result indicate that this risk adjusted model can provide better estimation for the risk adjustment model parameters. As expected, the proposed risk-adjusted control charts can achieve a better... 

    Detection of Multiple Change-point in Non-linear Profiles

    , M.Sc. Thesis Sharif University of Technology Khanzadeh, Mojtaba (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    This effort attempts to study the multiple change-point problem in the area of non-linear profiles. Two methods for estimating the times of multiple change-points is proposed. In the first method, a model consisting of two networks, which is based on artificial neural networks, is proposed. These networks are distinctive only in their training data. One network is trained for ascending segment of the profile and the other is trained for descending segments of the profile. In the second method, Bayesian approach is proposed for estimating multiple change-point. While using Bayesian approach the parameters of the Non-linear model must be estimated. However, this issue is complicated or... 

    Design of a Statistical Control Chart for Simultaneous Monitoring and Fault Isolation of Mean Vector and Covariance Matrix of Multivariate Multistage Processes

    , M.Sc. Thesis Sharif University of Technology Pirhooshyaran, Mohammad (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    In modern industries, multivariate multistage auto-correlated processes are widely used to ensure productivity and product quality. Interconnections between work stations bring a challenging task in detecting various shifts and identifying their root causes. In addition, simultaneous monitoring process mean and variability with single control chart methods has gained considerable attention throughout these years. In this article, a double-max multivariate exponentially weighted moving average (DM-MEWMA) chart is proposed based on two novel statistics to monitor the parameters of multivariate multistage auto-correlated processes jointly. Prior knowledge of variation propagation has been used... 

    Detecting and Estimating the Time of Single Step Change in Nonlinear Profiles

    , M.Sc. Thesis Sharif University of Technology Ghazizadeh Ahsaei, Ali (Author) ; Mahlooji, Hashem (Supervisor)
    Abstract
    This effort attempts to study the change point problem in the area of non-linear profiles. Two methods for estimating the time of a single step change is proposed. In the first method a model consisting of two networks which is based on artificial neural networks is proposed. These networks are different only in their training data. One network is trained for ascending segments of the profile and the other is trained for descending segments of the profile. In the second method the maximum likelihood estimator (MLE) of the single step change is analyzed. Due to the complexity of estimating the parameters of the non-linear model by MLE, this estimator is based on the difference between the... 

    Estimating Multiple Change Points in Multistage Processes

    , M.Sc. Thesis Sharif University of Technology Barati, Behzad (Author) ; Akhavan-Niaki, Taghi (Supervisor)
    Abstract
    Control charts are considered as one of the most important tools of statistical process control in detection of assignable causes of variation in the processes. One of the main criticisms of these charts is their inability in discovering the out-of-control state in real time. To eliminate the main sources of error, indicating the actual time of deviation in processes which is called change point is very important. Diagnosing of real time of changes limits the range of search for the causes of deviations and maximizes the chance of finding the main sources of deviation resulting in time saving and reducing expenses. There are different types of change points. One of change point types which... 

    Detecting and Estimating the Time of Change Point in Parameters Vector of Multi-Attribute Processes

    , M.Sc. Thesis Sharif University of Technology Khedmati, Majid (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    Control charts are one of the most important statistical process control tools used in monitoring processes and improving the quality by decreasing the variability of processes. In spite of various applications for multi-attribute control charts in industries and service sectors, only a few research efforts have been performed in developing this type of control charts. The developed multivariate control charts are all based on the assumption that the quality characteristics follow a multivariate Normal distribution while, in many applications the correlated quality characteristics that have to be monitored simultaneously are of attribute type and follow distributions such as multivariate... 

    A new monitoring design for uni-variate statistical quality control charts

    , Article Information Sciences ; Volume 180, Issue 6 , 2010 , Pages 1051-1059 ; 00200255 (ISSN) Fallah Nezhad, M. S ; Akhavan Niaki, S. T ; Sharif University of Technology
    Abstract
    In this research, an iterative approach is employed to analyze and classify the states of uni-variate quality control systems. To do this, a measure (called the belief that process is in-control) is first defined and then an equation is developed to update the belief recursively by taking new observations on the quality characteristic under consideration. Finally, the upper and the lower control limits on the belief are derived such that when the updated belief falls outside the control limits an out-of-control alarm is received. In order to understand the proposed methodology and to evaluate its performance, some numerical examples are provided by means of simulation. In these examples, the...