Industrial implementation of on-line multivariate quality control

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 T2 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.