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Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates...

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Autores principales: Zheng, Shuihua, Liu, Kaixin, Xu, Yili, Chen, Hao, Zhang, Xuelei, Liu, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038447/
https://www.ncbi.nlm.nih.gov/pubmed/32012753
http://dx.doi.org/10.3390/s20030695
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author Zheng, Shuihua
Liu, Kaixin
Xu, Yili
Chen, Hao
Zhang, Xuelei
Liu, Yi
author_facet Zheng, Shuihua
Liu, Kaixin
Xu, Yili
Chen, Hao
Zhang, Xuelei
Liu, Yi
author_sort Zheng, Shuihua
collection PubMed
description Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.
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spelling pubmed-70384472020-03-09 Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes Zheng, Shuihua Liu, Kaixin Xu, Yili Chen, Hao Zhang, Xuelei Liu, Yi Sensors (Basel) Article Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR. MDPI 2020-01-27 /pmc/articles/PMC7038447/ /pubmed/32012753 http://dx.doi.org/10.3390/s20030695 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zheng, Shuihua
Liu, Kaixin
Xu, Yili
Chen, Hao
Zhang, Xuelei
Liu, Yi
Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title_full Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title_fullStr Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title_full_unstemmed Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title_short Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
title_sort robust soft sensor with deep kernel learning for quality prediction in rubber mixing processes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038447/
https://www.ncbi.nlm.nih.gov/pubmed/32012753
http://dx.doi.org/10.3390/s20030695
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