Many industrial processes contain both linear and nonlinear parts, and kernel principal
component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the …

In order to deeply exploit intrinsic data feature information hidden among the process data,
an improved kernel principal component analysis (KPCA) method is proposed, which is …

A kernel independent component analysis (KICA) is widely regarded as an effective
approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based …

In the real-world batch process, the minor faults caused by aging equipment and catalyst
failure have subtle difference from normal data, making it difficult to monitor them timely with …

Kernel principal component analysis (KPCA) based fault detection method, whose statistical
model only utilizes normal operating data and ignores available prior fault information, may …

Industrial data are in general corrupted by noises and outliers. In this context, robustness to
the contaminated data is a challenging issue in process monitoring. In this article, a novel …

Independent component analysis (ICA) has been widely used in non-Gaussian multivariate
process monitoring. However, it assumes only one normal operation mode and omits the …

As an effective nonlinear dynamic data analysis tool, kernel slow feature analysis (KSFA)
has achieved great success in continuous process monitoring field during recent years …

Affected by the operation environment and uncertainties, batch processes have complex
dynamic characteristics, presenting autocorrelation and mutual correlation among process …

Most previous studies have shown that the multiphase characteristics of batch processes are
critical for process monitoring; however, revealing and utilizing the information of multiplicity …