Skewness Reduction Approach in Multi-Attribute Process Monitoring

By Seyed Taghi Akhavan Niaki, Babak Abbasi. 2007

53 citations recorded by the source corpus.

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 T 2 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 χ2 control chart.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5028726df327323b4cf9c8994e187439983dfd4e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. S. Ahmad
2. M. Abdollahian
3. Seyedeh Zahra Hosseinifard
4. Babak Abbasi

In Statistics and Probability

1. S. Ahmad
2. M. Abdollahian
3. Seyedeh Zahra Hosseinifard
4. Babak Abbasi

Citations in the corpus

• A transformation technique to estimate the process capability index for non-normal processes (2008, methodology): “Originally, Niaki and Abbasi [ 15 ] used the root transformation technique to design multi-attribute control charts.”