Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

Kernel principal component analysis (KPCA) is a well-established data-driven process
modeling and monitoring framework that has long been praised for its performances …

Data-driven process monitoring approaches like principal component analysis (PCA) have
been widely used in many industrial processes, most of which assume that the data are …

In advanced industrial fields such as chemical processes, faults must absolutely be
detected; we cannot afford to operate with failing operative parts. It is therefore, necessary to …

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image
applications. In this paper, a superpixelwise kernel principal component analysis …

Anomaly detection is critical to process modeling, monitoring, and control since successful
execution of these engineering tasks depends on access to validated data. The industrial …

To meet industrial demand, plenty of research works have been dedicated to monitoring the
health status of planetary gearboxes. For the same purpose, a new path is explored based …

Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels
can capture data nonlinearity by projecting the original variable space to a high-dimensional …

Data-driven is the use of data to drive knowledge and decisions. This has the potential to
produce better results but can also suboptimal based on a misinterpretation of data, faulty …

In industrial process monitoring, uncertainty in a system arises when measured data are not
representative of actual data. Uncertain information should be extracted to maintain safe …