2021
DOI: 10.3390/s21030823
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RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

Abstract: The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acqui… Show more

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Cited by 57 publications
(25 citation statements)
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“…In that sense, TL techniques can be categorised into three classes as a function of the data availability in the source and target domains or scenarios ([ 21 ] Chapter 4, [ 22 ]): Inductive Transfer Learning: In inductive transfer learning, the source and target domains do not show data scarcity problems. Therefore, the transfer model can be designed and firstly trained in the source domain and then fine-tuned in the target domain in order to adapt its behaviour to its final application.…”
Section: Methodsmentioning
confidence: 99%
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“…In that sense, TL techniques can be categorised into three classes as a function of the data availability in the source and target domains or scenarios ([ 21 ] Chapter 4, [ 22 ]): Inductive Transfer Learning: In inductive transfer learning, the source and target domains do not show data scarcity problems. Therefore, the transfer model can be designed and firstly trained in the source domain and then fine-tuned in the target domain in order to adapt its behaviour to its final application.…”
Section: Methodsmentioning
confidence: 99%
“…TL techniques have been adopted mainly to design and implement soft-sensors in those harsh environments, showing a lack of measurements. In [ 22 ], TL techniques were considered to design a soft-sensor which would be deployed over a sulphur recovery unit. The problem there is that this environment shows a severe problem of data scarcity; therefore, a traditional ANN training process cannot be performed.…”
Section: Introductionmentioning
confidence: 99%
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“…Each point in the sequence generates an internal signal fed through the neural network to the next layer. Hidden layers preserve information in the observed sequence and updates it in real-time [ 100 ]. Medical reports are typically processed by RNN.…”
Section: Major Techniques and Issuesmentioning
confidence: 99%
“…Though CNN has better data modelling power than DBN, it seems that the typical CNNs are not suitable for time-sequential modelling of data. Recurrent Neural Networks (RNNs) have the advantage over the aforementioned other models (e.g., DBN and CNN), which model time-sequential data obtained from different sources, such as sensors [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. However, typical RNNs have the limitation of vanishing gradient problems while modelling long sequences of data.…”
Section: Introductionmentioning
confidence: 99%