Industrial applications of digital twins
A digital twin (DT) is classically defined as the virtual replica of a real-world product, system,
being, communities, even cities that are continuously updated with data from its physical …
being, communities, even cities that are continuously updated with data from its physical …
[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 …
Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
Prediction of remaining useful life (RUL) is of vital significance in the prognostics health
management (PHM) tasks. To deal with the reverse time series and to reflect the difference …
management (PHM) tasks. To deal with the reverse time series and to reflect the difference …
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 …
Remaining useful life prediction of lithium-ion battery with adaptive noise estimation and capacity regeneration detection
As an indispensable energy device, 18650 lithium-ion battery has widespread applications
in electric vehicles. Remaining useful life (RUL) prediction of lithium-ion battery is critical for …
in electric vehicles. Remaining useful life (RUL) prediction of lithium-ion battery is critical for …
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition
Most supervised learning-based approaches follow the assumptions that offline data and
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty
Accurate prediction of the remaining useful life (RUL) of lithium-ion battery is of great
significance for the reliability of electronic equipment. In the conventional approaches, there …
significance for the reliability of electronic equipment. In the conventional approaches, there …
Learning deep multimanifold structure feature representation for quality prediction with an industrial application
Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …
conditions characteristics in the real industrial processes, the process data under different …
When deep learning-based soft sensors encounter reliability challenges: a practical knowledge-guided adversarial attack and its defense
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
A machine-learning architecture for sensor fault detection, isolation, and accommodation in digital twins
Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time
raw data into digital twins (DTs). However, sensors might be unreliable due to inherent …
raw data into digital twins (DTs). However, sensors might be unreliable due to inherent …