Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
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 …
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
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 …
Dynamic soft sensor development based on convolutional neural networks
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 …
quality-relevant variables in real time. In order to take advantage of the correlations between …
Soft sensor model for dynamic processes based on multichannel convolutional neural network
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 …
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …
Nonlinear dynamic soft sensor modeling with supervised long short-term memory network
X Yuan, L Li, Y Wang - IEEE transactions on industrial …, 2019 - ieeexplore.ieee.org
Soft sensor has been extensively utilized in industrial processes for prediction of key quality
variables. To build an accurate virtual sensor model, it is very significant to model the …
variables. To build an accurate virtual sensor model, it is very significant to model the …
Graph convolutional network soft sensor for process quality prediction
The nonlinear time-varying characteristics of the process industry can be modeled using
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
Flexible Clockwork Recurrent Neural Network for multirate industrial soft sensor
Data-driven-based soft sensors play significant roles in predicting key quality and optimizing
the production process. Considering the difficulty and cost of variable acquisition, these …
the production process. Considering the difficulty and cost of variable acquisition, these …
Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes
Z Geng, Z Chen, Q Meng, Y Han - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …
characteristics of high nonlinearity, dynamics, and noises. Many existing soft sensor …
Nonlinear dynamic soft sensor development with a supervised hybrid CNN-LSTM network for industrial processes
A soft sensor is a key component when a real-time measurement is unavailable for industrial
processes. Recently, soft sensor models based on deep-learning techniques have been …
processes. Recently, soft sensor models based on deep-learning techniques have been …
A novel bidirectional gated recurrent unit-based soft sensor modeling framework for quality prediction in manufacturing processes
L Ma, M Wang, K Peng - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Quality prediction is very important for improving the accuracy of quality control and the
stability of product quality in manufacturing processes. However, the complex time series …
stability of product quality in manufacturing processes. However, the complex time series …