1384368

11631

While principal component analysis (PCA) has found wide application in process monitoring, slow and normal process changes often occur in real processes, which lead to false alarms for a ®xed-model monitoring approach. In this paper, we propose two recursive PCA algorithms for adaptive process monitoring. The paper starts with an ecient approach to updating the correlation matrix recursively. The algorithms, using rank-one modi®cation and Lanczos tridiagonalization, are then proposed and their computational complexity is compared. The number of principal components and the con®dence limits for process monitoring are also determined recursively. A complete adaptive monitoring algorithm that addresses the issues of missing values and outlines is presented. Finally, the proposed algorithms are applied to a rapid thermal annealing process in semiconductor processing for adaptive monitoring.

471

Recursive principal component analysis , Adaptive process monitoring , Rank-one modi®cation , Lanczos tridiagonalization

Studia Iranica

486

486