Improving the performance of just-in-time learning based soft sensor through data augmentation

2022 ◽  
pp. 1-1
Author(s):  
Xiaoyu Jiang ◽  
Zhiqiang Ge
Keyword(s):  
Data Augmentation ◽  
Soft Sensor ◽  
Just In Time

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

2020 ◽  
Vol 99 (1) ◽  
pp. 334-344 ◽  
Author(s):  
Jianlin Wang ◽  
Kepeng Qiu ◽  
Yongqi Guo ◽  
Rutong Wang ◽  
Xinjie Zhou
Keyword(s):  
Soft Sensor ◽  
Batch Processes ◽  
Just In Time ◽  
Sensor Development

scholarly journals Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huaiping Jin ◽  
Jiangang Li ◽  
Meng Wang ◽  
Bin Qian ◽  
Biao Yang ◽  
...  
Keyword(s):  
Gaussian Process Regression ◽  
Soft Sensor ◽  
Viscosity Measurement ◽  
Finite Mixture ◽  
Just In Time ◽  
Mixing Process ◽  
Industrial Rubber ◽  
Viscosity Prediction ◽  
Mooney Viscosity ◽  
Input Variables

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.

Soft-sensor development using correlation-based just-in-time modeling

AIChE Journal ◽  
2009 ◽  
Vol 55 (7) ◽  
pp. 1754-1765 ◽  
Author(s):  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Shinji Hasebe ◽  
Akitoshi Takinami
Keyword(s):  
Soft Sensor ◽  
Just In Time ◽  
Sensor Development ◽  
Time Modeling
Sign in / Sign up

Export Citation Format

Share Document