Under the trend of economic globalization, intelligent manufacturing has attracted a lot of
attention from academic and industry. Related enabling technologies make manufacturing …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …

The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …

Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …

Abstract The Internet of Things benefits connectivity and functionality in industrial
environments, while Cloud Computing boosts computational capability. Hence, historical …

These days, data-driven soft sensors have been widely applied to estimate the difficult-to-
measure quality variables in the industrial process. How to extract effective feature …

For effective monitoring and control of the fermentation process, an accurate real-time
measurement of important variables is necessary. These variables are very hard to measure …

Probabilistic latent variable regression models have recently caught much attention in the
process industry, particularly for soft sensor applications. One of the main challenges for …

The digital wave of change had an unprecedented effect on the competitiveness of the
global value chain, whereby the process industries are not an exception. Although they are …