A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the
air handling unit (AHU) system's time-varying dynamic properties. However, since the …

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of
the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore …

Principal component analysis (PCA) is one of the most widely used techniques for process
monitoring. However, it is highly sensitive to sparse errors because of the assumption that …

Principal component analysis (PCA) is a classic fault diagnosis method widely used in
chemical process data modeling. However, the limitation of PCA to handle dynamic and time …

Process monitoring is one of the most crucial fundamental components in industrial
processes. Traditional multivariate statistical analysis modeling only relies on data collected …

In order to improve the performance of canonical correlation analysis (CCA) based methods
for process monitoring, this brief proposes a novel process monitoring approach using the …

Outliers may cause model deviation and then affect the monitoring performance and hence it
is a challenging problem for process monitoring. The robust principal component analysis …

Kernel principal component analysis (KPCA) has been widely applied to the nonlinear
process fault diagnosis field. However, it often does not perform well in the case of incipient …

Modern industrial processes are characteristic of nonstationary and uncertainty. To address
these issues, this paper proposes a probabilistic principal component analysis based model …