Visual Process Monitoring by Data-Dependent Kernel Discriminant Analysis with t-Distributed Similarities

Visual process monitoring would provide more directly appreciable and more easily comprehensible information about the process operating status as well as clear depictions of the occurrence path of faults; however, as a more challenging task, it has been sporadically discussed in the research literature on conventional process monitoring. In this paper, the Data-Dependent Kernel Discriminant Analysis (D²K-DA) model is proposed. A special data-dependent kernel function is constructed and learned from the measured data, so that the low-dimensional visualizations are guaranteed, combined with intraclass compactness, interclass separability, local geometry preservation, and global geometry preservation. The new optimization is innovatively designed by exploiting both discriminative information and t-distributed geometric similarities. On the construction of novel indexes for visualization, experiments of visual monitoring tasks on simulated and real-life industrial processes illustrate the merits of the proposed method.