Process nonlinearity and state shifting are two of the main factors that cause poor
performance of online soft sensors. Adaptive soft sensor is a common practice to ensure …

Industrial plants often undergo different kinds of changes like variable drifts and time‐variant
problems, which may cause the degradation of soft sensors. In this paper, a spatio‐temporal …

In this paper, the moving window (MW) approach is introduced to update the soft sensor
model with the latest process information, which provides powerful efficiency of tracking the …

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 …

A data-driven soft-sensor modelling approach based on dynamic kernel slow feature
analysis (KSFA) is proposed in this paper. Slow feature analysis is a feature extraction …

Soft sensor technology is an important means to estimate important process variables in real-
time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic …

Process nonlinearity and time-varying behavior of industrial systems are the main factors for
poor performance of online soft sensors. To ensure high predictive accuracy, adaptive soft …

Soft sensors have been widely used in chemical plants to estimate process variables that
are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive …

Local learning based soft sensing methods succeed in coping with time-varying
characteristics of processes as well as nonlinearities in industrial plants. In this paper, a …

Soft sensor technology has found widespread application in the real-time detection of
challenging variables like product quality and key process parameters. However, changes in …