An effective control scheme can be instrumental in increasing productivity and reducing cost. While facing an outlier-existing process, using the mean ( X ) control chart and the range (R) control chart for monitoring the process mean and variance will lead to high level false alarms. Recently, some median ( X~ ) control charts, such as the X~ Shewhart control chart, the exponentially weighted ...

Extended Abstract In many situations, the quality of a process can be characterized by a single continuous random variable, which is usually assumed to follow a normal distribution. However, it is increasingly common for processes to be characterized by several, usually correlated, variables. (Kim and Reynolds 2005) Multivariate control charts are widely used to monitor industrial processes (Ma...

Control chart pattern (CCP) recognition techniques are widely used to identify the potential process problems in modern industries. Recently, artificial neural network (ANN) –based techniques are very popular to recognize CCPs. However, finding the suitable architecture of an ANN-based CCP recognizer and its training process are time consuming and tedious. In addition, because of the black box ...

The Hotelling's  control chart, is the most widely used multivariate procedure for monitoring  two or more related quality characteristics, but it’s power lacks the desired performance in detecting small to moderate shifts. Recently, the variable sampling intervals (VSI) control scheme in which the length of successive sampling intervals is determined upon the preceding values has been proved t...

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...

neural networks because of their abilities are used to patterns recognition. in statistical process control charts, a common cause variation distort expected form of unnatural patterns and so detection of assignable causes efficiently and precisely in a real-time is difficult. therefore it would be logical to propose models based neural networks for recognition and analysis of patterns in proce...

Statistical Process Control (SPC) is a necessary part of modern chemical processing. The software chosen to collect quality data and produce control charts will determine whether SPC is an awkward task or a smoothly operating part of the process. The right software must satisfy all basic chemical production SPC needs, while providing additional capabilities which make it the core of a well run ...

In many processes in real practice at the start-up stages the process parameters are not known a priori and there are no initial samples or data for executing Phase I monitoring and estimating the process parameters. In addition, the practitioners are interested in using one control chart instead of two or more for monitoring location and variability of processes. In this paper, we consider a s...

Control charts such as X and R charts are widely used in industry to monitor quality. These charts are effective in detecting large departures from the in-control condition. Other process monitoring methods, such as Cumulative Sum (CUSUM) charts are sequential in nature as they accumulate information from previous observations. CUSUM charts are good at detecting more moderate presistent process...