Recent Development and Challenges of Soft Sensors Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 7121

Special Issue Editors


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Guest Editor
Department of Engineering, University of Messina, Contrada Di Dio, Vill. S. Agata , 98166 Messina, Italy
Interests: system identification; soft sensors; soft computing; machine learning; neural networks; nonlinear control; complex systems; industrial automation; process monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy
Interests: nonlinear systems modeling and control; bio-robotics; locomotion control, spiking neural networks, insect-inspired control systems; system identification and soft sensor development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The principles at the basis of Industry 4.0 presuppose continuous monitoring of processes and working conditions. An efficient measurement system is a fundamental pillar of this strategy. Physical and economic constraints are often in contrast with efficiency and cost reduction. In these scenarios, the possibility to adopt models, known as soft sensors (SSs), devoted to the estimation of process variables, represents a significant turning point.

The implementation of Soft Sensors for industrial processes historically represents an interesting field of applications for machine learning techniques. Advanced methodologies like long short-term memory, stacked autoencoders, convolutional neural networks, reservoir computing, and bio-inspired learning techniques have recently been proposed to improve SS behavior.

Though SSs are already widely used in industrial systems, and in particular in process industries, different aspects need to be investigated. Among these, we can consider SS design for time-variant systems, feature and data selection, outliers detection, big/small datasets, choice of the model class, model validation and maintenance, model interpretability, and transfer learning.

This Special Issue will focus on recent developments and challenges of soft sensor design both from a theoretical perspective and industrial applications.

Contributions related to the aforementioned topics are encouraged and a non-exhaustive list of topics can be found in the keywords.

Prof. Dr. Maria Gabriella Xibilia
Prof. Dr. Luca Patanè
Guest Editors

Manuscript Submission Information

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Keywords

  • Feature extraction 
  • Outliers detection 
  • Data selection 
  • Big and small datasets 
  • System identification 
  • Linear and nonlinear models 
  • Deep learning techniques 
  • Optimization strategies 
  • Recurrent neural networks 
  • Reservoir computing 
  • Bio-inspired learning techniques 
  • Model validation 
  • Soft sensor maintenance 
  • Transfer learning 
  • Model interpretability 
  • Sparse modeling 
  • Soft sensors for time-varying systems 
  • Industrial applications of soft sensors

Published Papers (3 papers)

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Research

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14 pages, 510 KiB  
Article
A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes
by Francisco Souza, Jérôme Mendes and Rui Araújo
Appl. Sci. 2021, 11(5), 2040; https://doi.org/10.3390/app11052040 - 25 Feb 2021
Cited by 7 | Viewed by 1862 | Correction
Abstract
This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were [...] Read more.
This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were compared when trained with different numbers of samples, and in comparison to other nonlinear predictive models. The models were evaluated on real multiphase polymerization process data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples. Full article
(This article belongs to the Special Issue Recent Development and Challenges of Soft Sensors Design)
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Review

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18 pages, 2926 KiB  
Review
Soft Sensor Transferability: A Survey
by Francesco Curreri, Luca Patanè and Maria Gabriella Xibilia
Appl. Sci. 2021, 11(16), 7710; https://doi.org/10.3390/app11167710 - 21 Aug 2021
Cited by 24 | Viewed by 3622
Abstract
Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation of real-time control and monitoring of the plants and present other advantages in terms of [...] Read more.
Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation of real-time control and monitoring of the plants and present other advantages in terms of costs and efforts. Given the complexity of industrial processes, these models are generally designed with data-driven black-box machine learning (ML) techniques. ML methods work well only if the data on which the prediction is performed share the same distribution with the one on which the model was trained. This is not always possible, since plants can often show new working conditions. Even similar plants show different data distributions, making SSs not scalable between them. Models should then be created from scratch with highly time-consuming procedures. Transfer Learning (TL) is a field of ML that re-uses the knowledge from one task to learn a new different, but related, one. TL techniques are mainly used for classification tasks. Only recently TL techniques have been adopted in the SS field. The proposed survey reports the state of the art of TL techniques for nonlinear dynamical SSs design. Methods and applications are discussed and the new directions of this research field are depicted. Full article
(This article belongs to the Special Issue Recent Development and Challenges of Soft Sensors Design)
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Other

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1 pages, 193 KiB  
Correction
Correction: Souza et al. A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes. Appl. Sci. 2021, 11, 2040
by Francisco Souza, Jérôme Mendes and Rui Araújo
Appl. Sci. 2021, 11(24), 11581; https://doi.org/10.3390/app112411581 - 07 Dec 2021
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Abstract
We, the authors, wish to make the following corrections to our paper [...] Full article
(This article belongs to the Special Issue Recent Development and Challenges of Soft Sensors Design)
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