Soft sensing of quality/key variables is critical to the control and optimization of industrial
processes. One of the main drawbacks of data-driven soft sensors is to deal with the …

In this work, we proposed a data-driven soft sensor based on deep learning techniques,
namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only …

Soft sensors have been extensively used to predict the difficult-to-measure key quality
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …

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 …

Industrial process data are usually time-series data collected by sensors, which have the
characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor …

The main purpose of soft sensor modeling is to capture the dynamic nonlinear features
between the easy-to-measure auxiliary variables and the difficult-to-measure key variables …

In industrial process control, some product qualities and key variables are always difficult to
measure online due to technical or economic limitations. As an effective solution, data …

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 …

Deep learning-based soft sensor has been a hot topic for quality variable prediction in
modern industrial processes. Feature representation with deep learning is the key step to …

Deep-learning-based soft sensors have been extensively developed for predicting key
quality or performance variables in industrial processes. However, most approaches …