Unpredicted faults occurring in automation systems deteriorate the performance of soft
sensors and may even lead to incorrect results. To address the problem, this study develops …

Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …

In manufacturing industry, process monitoring is a key to observe the product quality,
operational health, safety, and also for achieving good/satisfactory process control …

Soft metrology has been defined as a set of measurement techniques and models that allow
the objective quantification of properties usually determined by human perception such as …

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 …

In process industries, accurate prediction of critical quality variables is particularly important
for process control and optimization. Usually, soft sensors have been developed to estimate …

Data-driven soft sensors, estimating the pivotal quality variables, have been widely
employed in industrial process. This paper proposes a novel soft sensor modeling approach …

Dynamic data modeling has been attracting much attention from researchers and has been
introduced into the probabilistic latent variable model in the process industry. It is a huge …

As the production demand and external environment change, the same production process
may have multiple stable working conditions, ie, multimode process. The traditional process …

With the rapid development of cloud computing, edge computing, and deep learning
technologies, the implementation of soft sensor modeling within a cloud-edge collaboration …