Virtual metrology (VM) involves estimating a product's quality directly from production
process data without physically measuring it. This enables the product quality of each unit of …

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In
order to be able to provide a comprehensive overview of the adaptation techniques …

This multiauthor review article aims to bring readers up to date with some of the current
trends in the field of process analytical technology (PAT) by summarizing each aspect of the …

In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is
proposed for the monitoring and fault detection of multimode processes. A canonical …

Industrial process plants are instrumented with a large number of redundant sensors and the
measured variables are often contaminated by random noises. Thus, it is significant to …

Soft‐sensors have been widely used for estimating product quality or other key variables,
but their estimation performance deteriorate when the process characteristics change. To …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …

Increasing data-driven soft sensors have been adopted to online predict the quality indices
in polymerization processes to improve the availability of measurements and efficiency …

Virtual sensing technology is crucial for high product quality and productivity in any industry.
This review aims to clarify the trend of research and application of virtual sensing technology …

Just-in-time learning (JITL) is a widely used method for online soft sensing. The limitation of
available data and the increase of sample dimensions will make the historical dataset …