Incremental PCA based online model updating for multivariate process monitoring

Principle Component Analysis (PCA) has been used widely for process monitoring in industry systems. But the data drifting problem, which commonly exists in the actual process, disables the monitoring model, and subsequently makes the monitoring system come out with plenty of false alarm. Therefore the efficiency of PCA based process monitoring is degraded in practical use. This paper presents an incremental PCA based online model updating method for multivariate process monitoring. The proposed method is based on the characteristic that industry processes preserve the correlation between variables under normal production conditions, which enables the method update the direction of loading vectors as well as the mean value and the standard deviation of the model automatically. Our method has low computational complexity, limited storage demand and robust to normal data drifting. Finally, the performance of the proposed algorithm is compared with conventional PCA and EWMA-PCA methods on a benchmark dataset of semiconductor etch process, through which our method is proved to be efficient.