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Open AccessArticle

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

by 1,2, 1,2 and 1,2,*
1
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1957; https://doi.org/10.3390/s20071957
Received: 5 March 2020 / Revised: 24 March 2020 / Accepted: 25 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue How Intelligent Sensors Will Make a Difference in Industry 4.0)
Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high. View Full-Text
Keywords: Industrial 4.0; soft sensor; ensemble learning; active learning; hierarchical sampling; fermentation processes Industrial 4.0; soft sensor; ensemble learning; active learning; hierarchical sampling; fermentation processes
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Sheng, X.; Ma, J.; Xiong, W. Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes. Sensors 2020, 20, 1957.

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