Process dynamic behaviors resulting from closed-loop control and the inherence of
processes are ubiquitous in industrial processes and bring a considerable challenge for …

We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …

Deep generative models have demonstrated their effectiveness in learning latent
representation and modeling complex dependencies of time series. In this article, we …

Early detection of incipient faults in industrial processes is increasingly becoming important,
as these faults can slowly develop into serious abnormal events, an emergency situation, or …

Latent variable (LV) models have been widely used in multivariate statistical process
monitoring. However, whatever deviation from nominal operating condition is detected, an …

Abstract Process Systems Engineering (PSE) is now a mature field with a well-established
body of knowledge, computational-oriented frameworks and methodologies designed and …

Due to time-varying virtual drift in multimode processes, the performance of soft sensors will
degrade after online deployment. Traditional adaptive mechanisms have been developed to …

Due to unexpected data transition or equipment failures, irregular data with missing values,
which have both irregular sampling intervals and missing values, become very common in …

In industrial processes, soft sensor models are commonly developed to estimate values of
quality-relevant variables in real time. In order to take advantage of the correlations between …

Latent variable (LV) models provide explicit representations of underlying driving forces of
process variations and retain the dominant information of process data. In this study, slow …