Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load

Jian Zhang; Gang Yu; Jian Tang; Rongcheng Sun

doi:10.1117/12.3014557

8 December 2023 Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load

Jian Zhang, Gang Yu, Jian Tang, Rongcheng Sun

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430L (2023) https://doi.org/10.1117/12.3014557
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

During the process of mineral grinding, the ball mill generates the mechanical signals containing rich information due to different positions, which include mill loads and mill load parameters (MLPs). Actually, as one of the key factors, online MLPs detection is usually exploited to realize the intellectualization and improve the production efficiency of mineral processing plants. In this paper, on the basis of the ensemble empirical mode decomposition technique, the multisource and multicomponent mechanical signals of an experimental ball mill under changing ball, material, and water load conditions are analyzed to obtain different physical sub-signals via the intrinsic mode function.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Zhang, Gang Yu, Jian Tang, and Rongcheng Sun "Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430L (8 December 2023); https://doi.org/10.1117/12.3014557

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