This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and predict future poi...

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The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in process location and dispersion. For efficient monitoring of process variability, we present several CUSUM control charts for monitoring changes in standard deviation of a normal process. The newly developed control charts based on well-structured sampling techniques - extreme r...

We propose a Shewhart control chart based on gauging theoretically continuous observations into multiple groups. This chart is designed to monitor the process mean and standard deviation for deviations from stability. By assuming an underlying normal distribution, we derive the optimal grouping criteria that maximizes the expected statistical information available in a sample. Control charts ba...

Statistical Process Control (SPC) is used to monitor the process stability which ensures the predictability of the process. In 1920‟s Shewhart introduced the control chart techniques that are one of the most important techniques of quality control to detect if assignable causes exist. The widely used control chart techniques are R X  and S X  . These are called traditional variable control ch...

November 1, 2015 Version 0.5.2 Date 2015-10-31 Title Statistical Process Control -Collection of Some Useful Functions Author Sven Knoth Maintainer Sven Knoth Depends R (>= 1.8.0) Description Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts un...

The notion of variation and determining and eliminating it has become a subject that has found a wide application area after total quality approach has become widespread. There is a variety of control charts used in determining variation. Since the 1920’s, when W.Shewhart developed the first control chart, control charts, which have been examined many times in every aspect, are accepted to be o...

For attribute data with (very) low rates of defectives, attractive control charts can be based on the maximum of subsequent groups of r failure times, for some suitable r ≥ 1, like r = 5. Such charts combine good performance with often highly needed robustness, as they allow a nonparametric adaptation already for Phase I samples of ordinary size. In the present paper we address the problem of e...

Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction nonconforming. To overcome this difficulty, one may develop models based on the number of items inspected until r (P 1) nonconforming items are observed. The cumulative count con...