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

机译：集成高斯过程回归的分层采样策略的智能软传感器设计

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摘要

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.
机译：在工业4.0中，准确，实时的质量预测以具有竞争力的价格实现最佳过程控制是一个重要问题。本文展示了如何将智能软传感器与机器学习技术结合起来以显着节省人力资源并提高复杂工业条件下的性能的成功工程应用。基于集合学习的软传感器成功捕获了复杂的非线性，频繁的动态变化以及工业过程中的时变特性。但是，传统集成建模方法下的局部模型区域高度依赖于标记的数据样本，因此，当标记的样本受到限制时，其预测精度可能会受到影响。为了克服这一缺陷，提出了一种基于整体高斯过程回归（GPR）模型的新型主动学习（AL）框架，用于智能软传感器设计。首先，以基于分层抽样的AL策略迭代选择信息量最大的未标记样本，然后应用高斯混合模型（GMM）技术自主识别操作阶段，进一步构建无人参与的本地GPR模型，最后整合应用贝叶斯融合策略进行基础预测。青霉素发酵过程的比较研究证明了推荐的智能软传感的可靠性和优越性。人工标注的成本可以显着降低至少一半，同时预测性能保持较高水平。

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