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In order to reduce the variation in a manufacturing process, traditional statistical process control (SPC) techniques are the most frequently used tools in monitoring engineering process control (EPC)-controlled processes for detecting assignable cause process variation. Even though application of SPC with EPC can successfully detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this paper, we propose an independent component analysis-based signal extraction technique with classification and regression tree approach to identify disturbance levels in the correlated process parameters. For comparison, traditional cumulative sum (CUSUM) chart was constructed to evaluate the identifying capability of the proposed approach. The experimental results show that the proposed method outperforms CUSUM control chart in most instances. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks’ parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully. [ABSTRACT FROM AUTHOR]

Recently, a great deal of research has focused on integrating statistical process control (SPC) and engineering process control (EPC). Most of these studies have concluded that the integrated use of both SPC and EPC is superior in performance to the use of either alone. However, the majority of these studies have assumed that the assignable causes of a disturbance can be identified and removed as soon as the out-of-control signal is triggered by SPC. In practice, the identification of the assignable causes of a disturbance is not so straightforward. Using SPC and EPC control schemes, this study introduces a simple graphical aid technique to display the pattern of the underlying disturbance. Using EPC and a neural network (NN) scheme, this study focuses on the development of another technique to identify the nature of the assignable causes of the underlying disturbance. The effectiveness and the superiority of the proposed approaches are demonstrated through a series of simulations. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because data from these facilities are auto correlated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques arc needed to serve the same function as SPC control charts, that is to identify process shifts, in correlated parameters. Radial basis function neural networks were developed to identify shifts in process parameter values from papermaking and viscosity data sets available in the literature. Time series residual control charts were also developed for the data sets. Networks were successful at separating data that were shifted 1.5 and 2 standard deviations from nonshifted data for both the papermaking and viscosity parameter values. The network developed on the basis of the papermaking data set was also able to separate shifts of 1 standard deviation from nonshifted data. The SPC control charts were not able to identify the same process shifts. The radial basis function neural networks can be used to identify shifts in process parameters, thus allowing improved process control in manufacturing processes that generate correlated process data. [ABSTRACT FROM AUTHOR]