基于MEEMD-多尺度分形盒维数和ELM的球磨机负荷识别方法

doi:10.11949/j.issn.0438-1157.20180743

摘要/Abstract

摘要：

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。

关键词: MEEMD, 分形盒维数, 磨机负荷, 多尺度, ELM

Abstract:

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.

Key words: MEEMD, fractal box dimension, mill load, multi-scale, ELM

中图分类号:

TP 29

蔡改贫, 宗路, 刘鑫, 罗小燕. 基于MEEMD-多尺度分形盒维数和ELM的球磨机负荷识别方法[J]. 化工学报, 2019, 70(2): 764-771.

Gaipin CAI, Lu ZONG, Xin LIU, Xiaoyan LUO. Load identification method of ball mill based on MEEMD- multi-scale fractal box dimension and ELM[J]. CIESC Journal, 2019, 70(2): 764-771.

图/表 11

图1 原始信号

Fig.1 Raw signal

图2 模态分量的相关系数

Fig.2 Correlation coefficient of modal components

图3 磨音信号重构图

Fig.3 Reconfiguration of ground sound signal

表1 原始信号与重构信号的信噪比

Table1 Signal to noise ratio of original signal and reconstructed signal

信号类型

原始信号SNR/dB

重构信号SNR/dB

工况一

工况二

工况三

8.53

9.64

6.81

20.92

21.75

23.26

图4 两种算法的处理结果

Fig.4 Processing results of two algorithms

表2 三种算法处理后的信噪比

Table2 SNR of three algorithms after processing

算法类型

SNR/dB

EMD

EEMD

MEEMD

10.57

15.19

23.26

图5 三种负荷状态的多尺度分形盒维数

Fig.5 Fitting curves of cloud similarity values for all samples under normal load

图6 不同隐含神经元数量和激活函数的RMSE

Fig.6 RMSE of number and activation function of different hidden neurons

图7 训练的整体识别率

Fig.7 Training recognition rate

图8 测试的整体识别率

Fig.8 Overall recognition rate of the number and activation function of different hidden neurons

表3 不同负荷状态的识别准确率

Table 3 Recognition accuracy of different load states

负荷状态

识别准确率/%

欠负荷

正常负荷

过负荷

93.4

92.7

98.3

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