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 …
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 …
Novel deep learning based on data fusion integrating correlation analysis for soft sensor modeling
Accurate soft sensing modeling of complex industrial processes can provide operation
guidance for improving the product quality. However, most modeling methods cannot mine …
guidance for improving the product quality. However, most modeling methods cannot mine …
Deep learning based soft sensor and its application on a pyrolysis reactor for compositions predictions of gas phase components
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 …
namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only …
Data-driven soft sensor development based on deep learning technique
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 …
measure online due to technical or economic limitations. As an effective solution, data …
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 …
Active selection of informative data for sequential quality enhancement of soft sensor models with latent variables
With training data of insufficient information, soft sensor models inevitably show some
inaccurate predictions in their industrial applications. This work aims to develop an active …
inaccurate predictions in their industrial applications. This work aims to develop an active …
Efficient JITL framework for nonlinear industrial chemical engineering soft sensing based on adaptive multi-branch variable scale integrated convolutional neural …
Y Chen, A Li, X Li, D Xue, J Long - Advanced Engineering Informatics, 2023 - Elsevier
Just-in-time Learning (JITL) is a soft measurement method commonly used in industrial
processes, which can update local models in real-time to solve the problem of inaccurate …
processes, which can update local models in real-time to solve the problem of inaccurate …
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 …