A method to identify wet ball mill's load based on CEEMDAN, RCMDE and SRNN classification. (1st May 2021)

• Record Type: Journal Article
• Authors: Yang, Lirong; Cai, Jiacheng
• Is Part Of: Minerals engineering. Volume 165(2021)
• Publication Date: 2021-05-01
• Subjects: Ball mill's load -- CEEMDAN -- RCMDE -- SRNN; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2021.106852
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: An adaptive denoising algorithm-CEEMDAN is proposed to process vibration signal. Refined composite multi-scale dispersion entropy is proposed to extract features. SRNN is applied to establish the classification model of the ball mill load state. Experiments indicate that the recognition rate of ball mill load state is 98.67%. Abstract: Ball mill plays a key role in mineral processing plant, and its load identification for optimal control has great significance for the energy consumption reduction and production efficiency improvement. The vibration signal of ball mill shell contains abundant load information, which can be used to identify ball mill load. However, due to the non-linear and non-stationary characteristics of vibration signals, as well as the heavy background noises, the load identification becomes a challenging task in practice. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), and stacked recurrent neural network (SRNN) is proposed. First, CEEMDAN algorithm is used to decompose the ball mill's vibration signals and obtain the intrinsic mode function (IMF) components. Then, the sensitive IMF components are selected through the correlation coefficient method, and the signal is reconstructed with the sensitive IMF components. Secondly, the RCMDE of the reconstructed signal is calculated to obtain the load feature vector, and the dimension of the feature vector is reduced by principle component analysis (PCA). Thirdly, the SRNN is applied to establish a load recognition model, taking the feature vector as its input and the load state as its output. The experimental results show encouraging accuracy to apply this approach to recognize the wet ball mill's load, with a recognition rate of 98.67%.