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Experimental analysis of wet mill load parameter based on multiple channel mechanical signals under multiple grinding conditions

  • Published:2020-12 Issue: Volume:159 Page:106609
  • ISSN:0892-6875
  • Container-title:Minerals Engineering
  • language:en
  • Short-container-title:Minerals Engineering

Author:

Tang JianORCID,Yan Gaowei,Liu Zhuo,Liu Yefeng,Yu Gang,Sheng Ning

Funder

National Key R&D Program of the Ministry of Science and Technology

National Science Foundation of China

Beijing Natural Science Foundation

National & Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Publisher

Elsevier BV

Subject

Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fault Diagnosis of Rolling Bearing Based on CBAM-ICNN;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07

2. Multi-source unsupervised domain adaptive mill load forecasting method based on deep learning and fusion features;Minerals Engineering;2024-04

3. A Novel Bearing Fault Diagnosis Method Based on Wcdcgan with Cbam Attention Mechanism;2024

4. Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load;International Workshop on Signal Processing and Machine Learning (WSPML 2023);2023-12-08

5. Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals;International Workshop on Signal Processing and Machine Learning (WSPML 2023);2023-12-08
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