Developing Tensor-Based Common and Special Feature Analysis for Comprehensive Monitoring of Complex Batch Processes

Abstract

To overcome the limitation of unfolding-based methods and handle the multiple data set and limited data problems in the complex processes, such as multigrade batch processes, a novel tensor-based common and special feature extraction method and a comprehensive monitoring framework are proposed. In the proposed method, the uneven-length three-dimensional data are directly analyzed by the comprehensive tensor-based method without unfolding. To handle the multiple data set modeling problem, the tensor-based common feature extraction methods are first proposed to obtain the common features shared among different grades. The special features are sequentially determined by conducting tensor principal component analysis (PCA) on the residuals of each grade. The data are thus divided into common, special, and residual subspaces. Three monitoring statistics are established respectively in each subspace for online fault detection. The merits and effectiveness of the proposed method are demonstrated by an injection molding process with both even-length and uneven-length data in comparison with traditional methods.