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Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as T2, multivariate cumulative sum (MCUSUM)...
In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both...
In some practical situations, the quality of a process or product is characterized by a relationship (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we propose a supervised feed forward neural network to detect and classify drift shifts in linear profiles. The proposed method contains...
Quality characteristics are subject of both manufacturing and service industries, which include not only the variables but the attributes as well. In Quality Control area substantial research has been done for Auto-correlated variables; however, no attempt was done for Auto-correlated attributes. Ignoring the autocorrelation structure in constructing control charts cause the in-control run length...
An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN...
Statistical process control can have different objectives and can be done in different forms (Hawkins, et al, 2003). Currently, considerable attention has been given to the effect of data correlation on the statistical process control (SPC). The use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. This...
In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart SPC, artificial neural networks (ANN) have been widely used to recognize the abnormal pattern of control charts. But literature show that it is difficult for a developer to select the optimum NN topology architectures in a systemic way, this kind of work was primarily done according to the...
The control and monitoring of drinking water is becoming more and more interesting because of its effects on human life. Many techniques were developed in this field in order to ameliorate this process control attending to rigorous follow-ups of the quality of this vital resource. Several methods were implemented to achieve this goal. In this paper, a comparative study of two techniques resulting...
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