فهرست مقالات Change point estimation

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  1 - Change Point Estimation of a Process Variance with a Linear TrendDisturbance
  Rassoul Noorossana Majeed Heydari
  When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around th چکیده کامل

  When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always lead to successful identification of assignable cause(s). If process engineers could identify the change point, i.e. the time when the disturbance first manifested itself into the process, then corrective actions could be directed towards effective elimination of the source of disturbance. In this paper we develop a maximum likelihood estimator (MLE) for process change point designed to detect changes in process variance of a normal quality characteristic when the change follows a linear trend. We describe how this estimator can be used to identify the change point when a Shewhart S-control chart signals a change in the process variance. Numerical results reveal that the proposed estimator outperforms the MLE designed for step change when a linear trend disturbance is present. پرونده مقاله
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  2 - Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation
  Mohammad Reza Maleki Amirhossein Amiri Seyed Meysam Mousavi
  In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-contr چکیده کامل

  In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, first we use an artificial neural network (ANN)-based method in the literature for detecting the variance shifts as well as diagnosing the sources of variation in the multivariate-attribute processes. Then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ANN for estimating the time of step change in the multivariate-attribute process variability. We also compare the performance of the ANN-based estimator with the estimator based on maximum likelihood method (MLE). A numerical example based on simulation study is used to evaluate the performance of the estimators in terms of the accuracy and precision criteria. The results of the simulation study show that the proposed ANN-based estimator outperforms the MLE estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariate-attribute quality characteristics are manifested. پرونده مقاله