The identification of non-linear systems using only observed finite datasets has become a
mature research area over the last two decades. A class of linear-in-the-parameter models …

Probabilistic models, including probabilistic principal component analysis (PPCA) and
PPCA mixture models, have been successfully applied to statistical process monitoring. This …

This paper considers multivariate statistical monitoring of batch manufacturing processes. It
is known that conventional monitoring approaches, eg principal component analysis (PCA) …

The primary goal of multivariate statistical process performance monitoring is to identify
deviations from normal operation within a manufacturing process. The basis of the …

The integration of an ever-growing proportion of large-scale distributed renewable
generation has increased the probability of maloperation of the traditional RoCoF and vector …

This study explores the use of the nonlinear principal component analysis (NLPCA)
technique for detecting gas turbine faults. The resurgence of interest in neural network …

A novel nonlinear multivariate quality estimation and prediction method based on kernel
partial least-squares (KPLS) was proposed in this article. KPLS is a promising regression …

We study the generation and visualization of residuals for detecting and identifying unseen
faults using auto-associative models learned from process data. Least squares and kernel …

Nonlinear principal component analysis (PCA) based on neural networks has drawn
significant attention as a monitoring tool for complex nonlinear processes, but there remains …

High-dimensional data is involved in many fields of information processing. However,
sometimes, the intrinsic structures of these data can be described by a few degrees of …