Variational progressive-transfer network for soft sensing of multirate industrial processes
Deep-learning-based soft sensors have been extensively developed for predicting key
quality or performance variables in industrial processes. However, most approaches …
quality or performance variables in industrial processes. However, most approaches …
Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE
Soft sensors have been extensively used to predict difficult-to-measure quality variables for
effective modeling, control and optimization of industrial processes. To construct accurate …
effective modeling, control and optimization of industrial processes. To construct accurate …
A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling
Data-driven soft sensors have been widely adopted in industrial processes to learn hidden
knowledge automatically from process data, then to monitor difficult-to-measure quality …
knowledge automatically from process data, then to monitor difficult-to-measure quality …
Supervised attention-based bidirectional long short-term memory network for nonlinear dynamic soft sensor application
Soft sensors are mathematical methods that describe the dependence of primary variables
on secondary variables. A nonlinear characteristic commonly appears in modern industrial …
on secondary variables. A nonlinear characteristic commonly appears in modern industrial …
Deep learning for data modeling of multirate quality variables in industrial processes
Recently, deep-learning (DL)-based soft sensor has been widely applied to industrial
processes, which plays a vital role for process monitoring, control, and optimization …
processes, which plays a vital role for process monitoring, control, and optimization …
Improved Bi-LSTM with distributed nonlinear extensions and parallel inputs for soft sensing
Industrial soft sensing models have found extensive application in predicting key process
variables that are challenging to directly measure. However, the effectiveness of …
variables that are challenging to directly measure. However, the effectiveness of …
Data-driven soft sensing for batch processes using neural network-based deep quality-relevant representation learning
Soft sensors provide a means to reliably estimate unmeasurable variables, thereby playing
a prevalent role in formulating closed-loop control in batch processes. In soft sensor …
a prevalent role in formulating closed-loop control in batch processes. In soft sensor …
Quality prediction modeling for industrial processes using multiscale attention-based convolutional neural network
Soft sensors have been increasingly applied for quality prediction in complex industrial
processes, which often have different scales of topology and highly coupled spatiotemporal …
processes, which often have different scales of topology and highly coupled spatiotemporal …
Semi-supervised deep dynamic probabilistic latent variable model for multimode process soft sensor application
L Yao, B Shen, L Cui, J Zheng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Nonlinear and multimode characteristics commonly appear in modern industrial process
data with increasing complexity and dynamics, which have brought challenges to soft sensor …
data with increasing complexity and dynamics, which have brought challenges to soft sensor …
Feature representation-based cross-modality shared-specific network and its application in multimode process soft sensing
As the production demand and external environment change, the same production process
may have multiple stable working conditions, ie, multimode process. The traditional process …
may have multiple stable working conditions, ie, multimode process. The traditional process …