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 …

This paper proposes integrating slow feature analysis (SFA) with neural networks (SFA-NN)
for soft sensor development. Dynamic linear SFA is applied to the easy to measure process …

Latent variable (LV) models have been extensively constructed to obtain informative low-
dimensional features for process soft sensors. However, static LV models are more often …

An efficient nonlinear just-in-time learning (JITL) soft sensor method for online modeling of
batch processes with uneven operating durations is proposed. A recursive least-squares …

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 …

Soft sensors have been extensively applied for predicting difficult-to-measure quality
variables. However, industrial processes are often characterized with the nonlinearity and …

Currently, soft sensors have been extensively used in complex industrial processes. How to
learn effective features from nonlinear process data is the core task of building precise soft …

Soft sensors are vital for online predictions of quality-related yet difficult-to-measure
variables in process industry. In this paper, an adaptive soft sensing approach based on …

Modeling high‐dimensional dynamic processes is a challenging task. In this regard,
probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft …

The soft sensing of key performance indicators (KPIs) plays an essential role in the decision-
making of complex industrial processes. Many researchers have developed data-driven soft …