Soft sensor for ball mill load based on multi-view domain adaptation learning

Soft sensor for ball mill load based on multi-view domain adaptation learning

Guo, Xuqi (Taiyuan University of Technology)
Yan, F. (Taiyuan University of Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)
Yan, Gaowei (Taiyuan University of Technology)

2019

In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.

domain adaptation
mill load
multi-view
soft sensor
transfer learning

http://resolver.tudelft.nl/uuid:86d73127-8f0c-43aa-9c26-35b873696ee5

https://doi.org/10.1109/CCDC.2019.8832908

IEEE, Piscataway, NJ, USA

978-1-7281-0106-4

Proceedings of the 31st Chinese Control and Decision Conference (CCDC 2019)

31st Chinese Control and Decision Conference, CCDC 2019, 2019-06-03 → 2019-06-05, Nanchang, China

Accepted Author Manuscript

Institutional Repository

conference paper

© 2019 Xuqi Guo, F. Yan, Y. Pang, Gaowei Yan