基于MEEMD-多尺度分形盒维数和ELM的球磨机负荷识别方法

针对球磨机在磨矿过程中负荷(充填率、料球比)靠经验难以准确判断的问题，提出基于改进的集总平均经验模态分解算法(modified ensemble empirical mode decomposition, MEEMD)-多尺度分形盒维数盒和极限学习机(extreme learning machine，ELM)的负荷识别方法。该方法首先利用MEEMD算法对不同负荷状态下的磨音信号进行分解得到本征模态分量，然后，采用相关系数法选取敏感模态分量进行重构得到降噪后信号；通过分析重构信号的多尺度分形盒维数，结果表明，欠负荷、正常负荷和过负荷状态下的多尺度分形盒维数存在明显的差异，能够很好地区分磨机的不同负荷状态。将重构磨音信号的多尺度分形盒维数作为极限学习机(ELM)的输入，磨机负荷状态为输出，建立磨机负荷识别模型；通过磨矿实验验证了该方法的有效性，整体识别率高达94.8%，模型能够准确识别磨机负荷状态。

Load identification method of ball mill based on MEEMD- multi-scale fractal box dimension and ELM

In view of the problem that the load (filling rate and ball ratio) of a ball mill is difficult to be judged by experience during the grinding process, a method of mill load identification based on the multi-scale fractal box dimension of modified ensemble empirical mode decomposition (MEEMD) and extreme learning machine (ELM) is proposed. Firstly, the MEEMD algorithm is used to decompose the grind signals in different load states to get intrinsic mode components. Then, the correlation coefficient method is used to reconstruct the sensitive modal components to get the signal after noise reduction. By analyzing the multi-scale fractal box dimension of the reconstructed signal. The results show that there are obvious differences in the multi-scale fractal box dimensions of the under load, normal load and overloading state, and it can be well divided into different load states of the mill. The multi-scale fractal box dimension of regrinding signal is used as the input of extreme learning machine (ELM), and the load state of mill is output. The load identification model of mill is established. The effectiveness of the method is verified by grinding experiments. The recognition rate is as high as 94.8%, and the model can accurately identify the mill load status.