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

In terms of data-driven soft sensing modeling of industrial processes, it is practically
necessary to collect sufficient process data. Unfortunately, sometimes only few samples are …

Soft sensors have been extensively developed to estimate the difficult-to-measure quality
variables for real-time process monitoring and control. Process nonlinearities and dynamics …

Data-driven soft sensors have been widely used in industrial processes. Traditional soft
sensors are mostly shallow networks, which cannot easily describe the complicated process …

Here, this article reports about the design of a soft sensor (SS) able to monitor the hazardous
gases in industrial plants. The SS is designed to estimate the gas concentrations by means …

In modern industrial processes, dynamics and nonlinearities are two main difficulties for soft
sensing of key quality variables. Thus, nonlinear dynamic models like long short-term …

The industrial process often shows nonstationary characteristic, such as time-varying mean
and variance, due to the unmeasured disturbances, adjustments of production plans …

In modern industrial processes, soft sensors have played increasingly important roles for
effective process monitoring, control and optimization. Deep learning has shown excellent …

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 …

In this article, a novel data-driven industrial quality predictor is proposed based on the
cooperative deep dynamic feature extraction and variable time-delay (VTD) estimation. A …