Nonstationary Discrete Convolution Kernel for Multimodal Process Monitoring.

Data-driven process monitoring has benefited from the development and application of kernel transformations, especially when various types of nonlinearity exist in the data. However, when dealing with the multimodality behavior that is frequently observed in the process operations, the most widely used radial basis function (RBF) kernel has limitations in describing process data collected from multiple normal operating modes. In this article, we highlight this limitation via a synthesized example.