single-au.php

IJAT Vol.16 No.3 pp. 340-348
doi: 10.20965/ijat.2022.p0340
(2022)

Paper:

Study of the Load Forecasting of a Wet Mill Based on the CEEMDAN-Refined Composite Multiscale Dispersion Entropy and LSTM Nerve Net

Xiaoyan Luo*,**,†, Yaofeng Huang*, Fangwei Zhang*, and Qingling Wu*

*School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
86 Hongqi Dadao, Zhanggong, Ganzhou, Jiangxi 341000, China

Corresponding author

**Jiangxi Mining & Metallurgy Electromechanical Engineering Technology Research Center, Ganzhou, China

Received:
June 5, 2021
Accepted:
November 17, 2021
Published:
May 5, 2022
Keywords:
CEEMDAN, RCMDE, LSTM, mill load
Abstract

To address the problem of wet ball milling in a strong noise environment, it is difficult to accurately detect the internal load parameters of the cylinder during grinding. In this paper, a mill load parameter prediction method is proposed based on complementary ensemble empirical mode decomposition (CEEMDAN)-refined composite multiscale dispersion entropy (RCMDE) and-long and short-term memory (LSTM) neural networks. Using this method, the vibration signals of the mill barrel under strong noise were decomposed using the CEEMDAN algorithm, sensitive modal components with strong correlation with the original signal were selected for reconstruction using the correlation coefficient method, and features of the reconstructed signals under different load parameters were extracted through RCMDE. The load characteristic vector of an RCMDE mill was used as the input of LSTM neural networks, and the filling rate, material and ball ratio, and grinding concentration were used as the output to establish the internal load prediction model of wet mill. Experiment results show that the prediction method has a high accuracy, with average absolute percentage errors of the filling rate, feed-to-ball ratio, and grinding concentration of 6.08%, 3.50%, and 3.47%, and average absolute errors were of 0.0167, 0.0146, and 0.0146, respectively.

Cite this article as:
X. Luo, Y. Huang, F. Zhang, and Q. Wu, “Study of the Load Forecasting of a Wet Mill Based on the CEEMDAN-Refined Composite Multiscale Dispersion Entropy and LSTM Nerve Net,” Int. J. Automation Technol., Vol.16, No.3, pp. 340-348, 2022.
Data files:
References
  1. [1] Y. Sha, T. Chang, and J. Chang, “Measure Methods of Ball Mill’s Load,” Modern Electric Power, 2006.
  2. [2] S. P. Das, D. P. Das, S. K. Behera, and B. K. Mishra, “Interpretation of mill vibration signal via wireless sensing,” Minerals Engineering, Vol.24, Issues 3-4, pp. 245-251, 2010.
  3. [3] J. Tang, L.-J. Zhao, J.-W. Zhou, H. Yue, and T.-Y. Chai, “Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell,” Minerals Engineering, Vol.23, Issue 9, pp. 720-730, 2010.
  4. [4] G. Cai, L. Zong, X. Liu, and X. Luo, “Load identification method of ball mill based on MEEMD-multi-scale fractal box dimension and ELM,” CIESC J., Vol.70, No.2, pp. 764-771, 2019 (in Chinese).
  5. [5] J. Tang, T. Chai, W. Yu, and L. Zhao, “Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information,” IEEE Trans. on Automation Science and Engineering, Vol.10, No.3, pp. 726-740, 2013.
  6. [6] B. Behera, B. K. Mishra, and C. V. R. Murty, “Experimental analysis of charge dynamics in tumbling mills by vibration signature technique,” Minerals Engineering, Vol.20, Issue 1, pp. 84-91, 2007.
  7. [7] J. Chen, D. Jiang, and Y. Zhang, “A common spatial pattern and wavelet packet decomposition combined method for EEG-based emotion recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 274-281, 2019.
  8. [8] N. Hu, H. Chen, Z. Cheng, L. Zhang, and Y. Zhang, “Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks,” J. of Mechanical Engineering, Vol.55, No.7, pp. 9-18, 2019 (in Chinese).
  9. [9] H. Jin and J. Lin, “Structural modal system identification using variational mode decomposition and Teager energy operators,” J. of Vibration, Measurement & Diagnosis, Vol.39, No.3, pp. 544-551+670-671, 2019 (in Chinese).
  10. [10] C. Huang, X. Wu, W. Cao, Y. Meng, and J. Li, “LMD-based full vector envelope technique and its application in TRT vibration fault diagnosis,” Electric Power Automation Equipment, Vol.35, No.2, pp. 168-174, 2015 (in Chinese).
  11. [11] Y. Yao, S. Sfarra, C. Ibarra-Castanedo, R. You, and X. P. V. Maldague, “The multi-dimensional ensemble empirical mode decomposition (MEEMD),” J. of Thermal Analysis and Calorimetry, Vol.128, No.3, pp. 1841-1858, 2017.
  12. [12] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: A noise-assisted data analysis method,” Advances in Adaptive Data Analysis, Vol.1, No.1, pp. 1-41, 2009.
  13. [13] M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” Proc. of 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 4144-4147, 2011.
  14. [14] J.-R. Yeh, J.-S. Shieh, and N. E. Huang, “Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method,” Advances in Adaptive Data Analysis, Vol.2, No.2, pp. 135-156, 2010.
  15. [15] J.-D. Zheng, J.-S. Cheng, and Y. Yang, “Modified EEMD algorithm and its applications,” J. of Vibration and Shock, Vol.32, No.21, pp. 21-26+46, 2013 (in Chinese).
  16. [16] L. Si, Z. Wang, X. Liu, and C. Tan, “A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine,” Engineering Applications of Artificial Intelligence, Vol.78, pp. 86-101, 2019.
  17. [17] T. Wang, X. Yue, X. Gu, S. Zhang, and B. Zhao, “Power grid critical node identification based on singular value entropy and power flow distribution entropy,” Electric Power Automation Equipment, Vol.36, No.4, pp. 46-53, 2016 (in Chinese).
  18. [18] J. Zheng, H. Pan, and J. Cheng, “Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines,” Mechanical Systems and Signal Processing, Vol.85, pp. 746-759, 2017.
  19. [19] X. Hu, G. Cai, X. Luo, and L. Zong, “Load identification method for ball mills based on CEEMDAN and multi-scale permutation entropy,” Noise and Vibration Control, Vol.38 No.3, pp. 146-151, 2018 (in Chinese).
  20. [20] J. Zheng, J. Cheng, and S. Hu, “Rotor fault diagnosis based on multiscale entropy,” J. of Vibration, Measurement & Diagnosis, Vol.33, No.2, pp. 294-297+342, 2013 (in Chinese).
  21. [21] M. Rostaghi and H. Azami, “Dispersion entropy: A measure for time series analysis,” IEEE Signal Processing Letters, Vol.23, No.5, pp. 610-614, 2016.
  22. [22] H. Azami, M. Rostaghi, D. Abásolo and J. Escudero, “Refined composite multiscale dispersion entropy and its application to biomedical signals,” IEEE Trans. on Biomedical Engineering, Vol.64, No.12, pp. 2872-2879, 2017.
  23. [23] B. Alić, D. Sejdinović, L. Gurbeta, and A. Badnjevic, “Classification of stress recognition using Artificial Neural Network,” Proc. of 2016 5th Mediterranean Conf. on Embedded Computing (MECO), pp. 297-300, 2016.
  24. [24] S. V. Pchelintseva, A. E. Runnova, V. Y. Musatov, and A. E. Hramov, “Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach,” Proc. SPIE 10063, Dynamics and Fluctations in Biomedical Photonics XIV, Article No.1006317, 2017.
  25. [25] J. Herwan, S. Kano, O. Ryabov, H. Sawada, N. Kasashima, and T. Misaka, “Predicting surface roughness of dry cut grey cast iron based on cutting parameters and vibration signals from different sensor positions in CNC turning,” Int. J. Automation Technol., Vol.14, No.2, pp. 217-228, 2020.
  26. [26] T. Ohkubo, E. Matsunaga, J. Kawanaka, T. Jitsuno, S. Motokoshi, and K. Yoshida, “Recurrent neural network for predicting dielectric mirror reflectivity,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.6, pp. 1012-1018, 2019.
  27. [27] L. Liu, Q. Wang, and Y. Li, “Improved Chinese sentence semantic similarity calculation method based on multi-feature fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, pp. 442-449, 2021.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 22, 2022