The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

中文翻译：

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用于工业橡胶混合过程中门尼粘度预测的集成即时学习软传感器

缺乏用于门尼粘度测量的在线传感器对实现工业橡胶混炼过程的有效监测、控制和优化提出了重大挑战。为了获得门尼粘度的实时和准确估计，通过利用集成提出了一种新的软传感器方法，称为基于多模态扰动（MP-）的集成即时学习高斯过程回归（MP-EJITGPR）即时学习。该方法利用对相似性度量和输入变量的扰动来生成 JIT 学习器的多样性。此外，通过明确平衡准确性和多样性目标，通过进化多目标优化构建了一组准确且多样化的 JIT 学习器。此外，所有基础 JIT 学习器都使用有限混合机制自适应地组合。