Industrial implementation of on-line multivariate quality control

Abstract: Dow Chemical is committed to product quality consistency. To achieve this goal, a project on implementing multivariate quality control was launched. Robust principal component analysis (PCA) was applied to a historical data set of product quality to remove outliers. The remaining data, representing normal process variation, were used to build a PCA model. One advantage of multivariate analysis is that PCA can detect a change in variable correlation, which is undetectable using univariate control charts. The T 2 statistic is used for fault detection and the contribution chart is used for fault identification. The model has been implemented on-line and proven to be effective in detecting and identifying abnormal product lots and analytical issues. Practical issues such as long-term model performance, model maintenance, and model transfer are discussed in this paper. [Copyright &y& Elsevier]