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

Conference paper (2019)

Authors

Xuqi Guo Taiyuan University of Technology
F. Yan Taiyuan University of Technology
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Gaowei Yan Taiyuan University of Technology
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 Xuqi Guo, F. Yan, Y. Pang, Gaowei Yan
DOI
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https://doi.org/10.1109/CCDC.2019.8832908
Domain adaptation Transfer learning Soft sensor Mill load Multi-view
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Published Date

2019

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

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.