[HTML][HTML] The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review
With the predicted depletion of natural resources and alarming environmental issues,
sustainable development has become a popular as well as a much-needed concept in …
sustainable development has become a popular as well as a much-needed concept in …
[HTML][HTML] From fully physical to virtual sensing for water quality assessment: A comprehensive review of the relevant state-of-the-art
Rapid urbanization, industrial development, and climate change have resulted in water
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
A survey on deep learning for data-driven soft sensors
Q Sun, Z Ge - IEEE Transactions on Industrial Informatics, 2021 - ieeexplore.ieee.org
Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …
prediction, and many other important applications. With the development of hardware and …
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 …
A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data
X Yuan, S Qi, Y Wang, H Xia - Control Engineering Practice, 2020 - Elsevier
Hierarchical local nonlinear dynamic feature learning is of great importance for soft sensor
modeling in process industry. Convolutional neural network (CNN) is an excellent local …
modeling in process industry. Convolutional neural network (CNN) is an excellent local …
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Abstract Stacked auto-encoder (SAE)-based deep learning has been introduced for fault
classification in recent years, which has the potential to extract deep abstract features from …
classification in recent years, which has the potential to extract deep abstract features from …
Augmented multidimensional convolutional neural network for industrial soft sensing
In the era of industrial big data, data-driven soft-sensor models have become an important
method to guide production and optimize control. However, due to the limitation of data …
method to guide production and optimize control. However, due to the limitation of data …
[HTML][HTML] Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic …
Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials,
fintech management, medicine, neurorobotics, and psychology, among others. Prognostics …
fintech management, medicine, neurorobotics, and psychology, among others. Prognostics …
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 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 …