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Showing 1 to 7 of 7 for “"Shewhart Control Charts"”.
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An economic evaluation of variable sampling interval Shewhart control charts for means
… process with a Variable Sampling Interval control chart. This chart has the feature that the time delay between successive samples is not constant but depends on the result of the most recent sample observation. The model assumes that the quality characteristic of interest is distributed …
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Statistical control and experimental design for edge bead reduction in laminating process
… process capability was calculated. After that, Shewhart control charts were employed to identify the assignable causes and to bring the process to in-control state. Then, a designed experiment was conducted to find the optimal operating conditions. It was found that line speed, screw speed, …
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Variable sampling in multiparameter Shewhart charts
This dissertation deals with the use of Shewhart control charts, modified to have variable sampling intervals, to simultaneously monitor a set of parameters. Fixed sampling interval control charts are modified to utilize sampling intervals that vary depending on what is being observed from the …
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Improvement of cleaning effectiveness through Statistical Process Control in active pharmaceutical ingredient (API) manufacturing
… to be a resultant of inadequate process control and monitoring. Statistical Process Control (SPC) implemented using process automation was found to improve the success rate of cleaning processes significantly. SPC introduction required breaking down the cleaning operation into component …
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Efficient Sampling Plans for Control Charts When Monitoring an Autocorrelated Process
… the performances of various sampling plans for control charts in detecting special causes that may produce sustained or transient shifts in the process mean and/or variance. Observations from the process are modeled as a first-order autoregressive process plus a random error. Combinations of two …
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Statistical process control by quantile approach.
Most quality control and quality improvement procedures involve making assumptions about the distributional form of data it uses; usually that the data is normally distributed. It is common place to find processes that generate data which is non-normally distributed, e.g. Weibull, logistic or …
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Advanced computational techniques for pipe burst detection and localisation in water distribution networks
Pipe bursts cause a considerable loss of treated water, increase the risks of environmental contamination and are a health hazard for the end-user as they can create a passage for contaminants to enter water distribution networks (WDN). Identifying pipe burst locations will help water service …