Abstract
Development of an efficient process monitoring system has always received great attention. Previous studies revealed that the coefficient of variation (CV) is important in ensuring process quality, especially for monitoring a process where its process mean and variance are highly correlated. The fact that almost all industrial process monitoring involves a minimum of two or more related quality characteristics being monitored simultaneously, this paper incorporates the salient feature of the adaptive sample size VSS scheme into the standard multivariate CV (MCV) chart, called the VSS MCV chart. A Markov chain model is developed for the derivation of the chart’s performance measures, i.e the average run length (ARL), the standard deviation of the run length (SDRL), the average sample size (ASS), the average number of observations to signal (ANOS) and the expected average run length (EARL). The numerical comparison shows that the proposed chart prevails over the existing standard MCV chart for detecting small and moderate upward and downward MCV shifts.
Highlights
Control charting techniques are common-used techniques in the process signal detection for the purpose of improving the quality of manufacturing and service processes
For ensuring a fair comparison with the Std multivariate CV (MCV) chart in terms of the average run length (ARL) and expected average run length (EARL) criteria, the ASS0 of the variable sample size (VSS) MCV chart is set as n0
The results show that the VSS MCV chart outperforms the Std MCV chart, for detecting both the small and moderate upward and downward MCV shifts, in terms of the ARL1, SDRL1 and EARL1 criteria
Summary
Control charting techniques are common-used techniques in the process signal detection for the purpose of improving the quality of manufacturing and service processes. Most of the control charts are used to monitor the process mean and variance, subject to the process mean is constant while the process standard deviation is independent of the mean. The mean and standard deviation of some processes are not independent of each other, this may cause erroneous conclusions. To circumvent this drawback, the coefficient of variation (CV) control chart is preferred to be used. Babu and Sudha [7] applied the CV for reducing the speckle noise in ultrasound images. According to Karthik and Manjunath [23], the CV can be used to correct the unduly non-uniform distance between grid lines in noisy microarray images. The CV can be implemented to ISSN 2310-5070 (online) ISSN 2311-004X (print)
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