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Skewness reduction approach in multi-attribute process monitoring

Akhavan Niaki , S. T ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1080/03610920701215456
  3. Publisher: 2007
  4. Abstract:
  5. Since the product quality of many industrial processes depends upon more than one dependent variable or attribute, they are either multivariate or multi-attribute in nature. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this article, we develop a new methodology to monitor multi-attribute processes. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewness. Then, we estimate the transformed covariance matrix and apply the well-known T2 control chart. In order to illustrate the proposed method and evaluate its performance, we use two simulation experiments and compare the results with the ones from both MNP chart and the 2 control chart
  6. Keywords:
  7. Covariance matrix ; Mathematical transformations ; Multivariant analysis ; Product development ; Quality control ; Statistical process control ; Multi-attribute control charts ; Multi-binomial distribution ; Skewness reduction ; Process monitoring
  8. Source: Communications in Statistics - Theory and Methods ; Volume 36, Issue 12 , 2007 , Pages 2313-2325 ; 03610926 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/03610920701215456