Uncorrelated component analysis on manifold for statistical process monitoring

A novel process modeling approach referred to as locally invariant uncorrelated component analysis (LIUCA) is proposed for the purpose of statistical process monitoring. The contributions are as follows: (1) LIUCA intends to find a part‐based representation subspace in which two data points are close to each other, if they are close in the k ‐nearest neighbor graph; (2) LIUCA can exploit the geometrical structure of the data space, which will improve the algorithm's modeling performance in real‐world applications; (3) LIUCA‐based multivariate statistical process monitoring scheme is proposed. (4) In contrast to traditional process modeling algorithm such as principal component analysis, LIUCA imposes no restriction on data distribution. In addition, both a multivariate numerical example and a hot galvanizing pickling waste liquor treatment process are taken to evaluate the feasibility of the proposed process monitoring scheme. Experiment results demonstrate the effectiveness of the proposed method.