Aiming at problems of ball mill’s load being difficult to detect and load state being not able to be determined in process of ball mill grinding ore,a prediction model for mill load based on IEDA-cloud model feature entropy and LSSVM was proposed. The integrated empirical decomposition algorithm (IEDA) was used to decompose vibration signals of a ball mill under different loads. The sensitive modal components were chosen using the correlation coefficient method to reconstruct a signal. The inverse cloud generator was used to calculate the cloud model’s feature entropy of the reconstructed signal as its feature parameter. The forward cloud generator was used to generate the cloud drop diagram of the cloud model feature vector. The results showed that differences among entropy values of under-load state,normal-load one and over-load one are large,and they can be used to better distinguish and identify ball mill’s load states. The feature vector of the cloud model was taken as the input of a least squares support vector machine (LSSVM),ratio of material to ball and filling rate were taken as the output to establish a prediction model for ball mill’s load,the effectiveness of the proposed model was verified by tests of grinding ore,it was shown that the proposed model can be used to correctly predict load state of ball mills.
蔡改贫,宗路,罗小燕,胡显能. 基于CEEMDAN-云模型特征熵和LSSVM的磨机负荷预测研究[J]. 振动与冲击, 2019, 38(7): 128-133.
CAI Gaipin, ZONG Lu, LUO Xiaoyan, HU Xianneng. Prediction of ball mill’s load based on IEDA-cloud model feature entropy and LSSVM. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(7): 128-133.
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