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

Xuqi Guo, Fei Yan, Yusong Pang, Gaowei Yan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.

Original languageEnglish
Title of host publicationProceedings of the 31st Chinese Control and Decision Conference (CCDC 2019)
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-7281-0105-7
ISBN (Print)978-1-7281-0106-4
Publication statusPublished - 2019
Event31st Chinese Control and Decision Conference, CCDC 2019 - Nanchang, China
Duration: 3 Jun 20195 Jun 2019


Conference31st Chinese Control and Decision Conference, CCDC 2019


  • domain adaptation
  • mill load
  • multi-view
  • soft sensor
  • transfer learning

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