In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …

Risks in chemical plants can generally be divided into Black Swan incidents and Gray Rhino
incidents. Black Swan events are unexpected and have a significant impact. Frequently, a …

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials,
fintech management, medicine, neurorobotics, and psychology, among others. Prognostics …

A well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack
of robustness against outliers. This shortcoming hinders the application of CCA in the case …

Complex industrial processes are commonly characterized with multiple operation modes.
The existing manifold learning-based process monitoring methods describe each mode …

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 …

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 …

Most industrial batch processes involve inherent dynamic characteristics in both within-batch
time direction and batch-wise direction. In order to ensure process safety and improve …

In a large-scale industrial system with numerous variables, the relations among variables
are often nonlinear and very complicated, due to material, energy and information flows …