Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals

Jian Zhang; Gang Yu; Jian Tang; Pengbo Liu

doi:10.1117/12.3014556

8 December 2023 Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals

Jian Zhang, Gang Yu, Jian Tang, Pengbo Liu

Author Affiliations +

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430K (2023) https://doi.org/10.1117/12.3014556
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

During the process of mineral grinding, mill load parameters (MLPs) determine the information of the mechanical signals. So, online MLPs detection is one of the key factors for improving the production efficiency of mineral processing plants. In this paper, the correlation between the multichannel mechanical signal and the different MLPs is explored by the power spectral density. Furthermore, the contribution rate of the multisource and multicomponent mechanical signals to the MLPs and mill load is measured on the basis of the correlation coefficient. Finally, a prediction model for MLPs can be constructed according to an adaptive decomposition strategy and the appropriate sub-signals.

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

Citation Download Citation

Jian Zhang, Gang Yu, Jian Tang, and Pengbo Liu "Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430K (8 December 2023); https://doi.org/10.1117/12.3014556

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