CEEMDAN和样本熵相结合的球磨机负荷识别方法

刘吉顺; 杨丽荣; 罗小燕; 程铁栋

doi:10.13433/j.cnki.1003-8728.20200037

CEEMDAN和样本熵相结合的球磨机负荷识别方法

doi: 10.13433/j.cnki.1003-8728.20200037

江西理工大学机电工程学院，江西赣州　341000

基金项目: 国家自然科学基金项目（51464017）、江西省重点研发计划项目（20181ACE50034）与江西省教育厅科技项目（GJJ190452）

详细信息

作者简介:
刘吉顺（1995−），硕士研究生，研究方向为物料高效破碎，2621718365@qq.com

通讯作者:
杨丽荣，副教授，硕士，18978844@qq.com

中图分类号: TD921
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- 文章访问数: 182
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-17
- 刊出日期: 2021-02-02

A Ball Mill Load State Identification Method in Combination with CEEMDAN and Sample Entropy

School of Mechanical and Electrical Engineering, Jiangxi University of Technology, Ganzhou 341000, Jiangxi, China

摘要

摘要: 针对磨矿作业过程中球磨机的筒体内部情况复杂难以仅靠经验估计负荷状态的问题，提出一种自适应白噪声的完整经验模态分解(CEEMDAN)结合样本熵和极限学习机(ELM)的球磨机负荷状态识别方法。首先，通过CEEMDAN算法对不同负荷状态下原始信号进行分解，利用相关系数法筛选有效的IMF分量；然后，通过分析3种负荷状态下振动信号的有效IMF分量的样本熵在不同数据长度、嵌入维度和相似容限下的值，来确定计算样本熵的最佳参数。结果表明，3种负荷状态下振动信号的有效IMF分量样本熵有明显区别，可以有效识别出磨机不同负荷状态。将各组信号有效IMF分量样本熵作为ELM的输入，球磨机负荷状态为输出，建立了磨机负荷状态识别模型。利用磨矿实验进行验证，表明此种方法应用在球磨机负荷识别上的有效性，整体识别率高达96.81%，且对比于EMD-样本熵和MEEMD-样本熵，总体识别率分别提高了12.41%和9.01%。
- CEEMDAN /
- 样本熵 /
- 相关系数 /
- ELM /
- 负荷状态识别
Abstract: Owing to the complex internal condition of a ball mill in its grinding process, it is difficult to estimate its load state with experience alone. For the complete empirical mode decomposition (CEEMDAN) of adaptive white noise combined with sample entropy and extreme learning machine (ELM), the paper proposes a ball mill load state identification method. Firstly, the eigenmode decomposition of the original signal under different load conditions is carried out withthe CEEMDAN algorithm, and the effective IMF component is filtered with the correlation coefficient method. Then, the sample entropy of the effective IMF component of the vibration signal under three load states is analyzed for different data. The values for length, embedded dimension and similar tolerance determine the optimal parameters for calculating sample entropy. The results show that there are significant differences in the effective IMF component sample entropy of vibration signals under three load states, which can be used to effectively identify the different load states of the ball mill. The effective IMF component sample entropy of each group of signals is taken as the input of ELM and the load states of the ball mill as output, and the load state recognition model is established. The grinding test is used to verify the effectiveness of this method in theball mill load state recognition. The overall recognition rate is as high as 96.81% and increases by 12.41% compared with that of EMD-sample entropy and MEEMD-sample entropy, namely 9.01%.
- CEEMDAN /
- sample entropy /
- correlation coefficient /
- extreme learning machine (ELM) /
- load state recognition

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图 1 实验装置图

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图 2 原始信号的波形图

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图 3 模态分量相关系数误差棒图

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图 4 振动信号重构图

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图 5 欠负荷筒体振动重构信号

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图 6 不同负荷状态信号样本熵随数据长度的变化图

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图 7 不同负荷状态信号样本熵随相似容限变化图

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图 8 不同隐含层神经元个数和激活函数的RMSE

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图 9 模型测试识别率图

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表 1 不同负荷信号去噪效果比较

负荷状态	原始信号SNR/dB	重构信号SNR/dB
欠负荷	9.12	24.52
正常负荷	10.36	25.53
过负荷	8.25	27.41

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表 2 不同算法处理后的信噪比

算法	SNR/dB
EEMD	11.23
MEEMD	24.35
CEEMDAN	27.41

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表 3 不同负荷状态五组信号的有效IMF分量样本熵平均值

磨机负荷状态	IMF分量
磨机负荷状态	IMF₁	IMF₂	IMF₃	IMF₄
欠负荷	1.2542	0.2564	0.5895	0.4562
正常负荷	0.9523	0.1245	0.3526	0.2564
过负荷	0.5426	0.0254	0.1257	0.0859

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表 4 不同负荷状态的部分信号IMF分量样本熵值

负荷状态	IMF分量
负荷状态	IMF₁	IMF₂	IMF₃	IMF₄
欠负荷	1.1379	0.2133	0.6275	0.4689
	1.0596	0.2461	0.4853	0.4437
	1.2255	0.2831	0.3955	0.4026
	1.2630	0.0273	0.3978	0.4020

正常负荷	0.7150	0.1562	0.3413	0.2199
	0.6109	0.1228	0.3238	0.4099
	1.0493	0.1829	0.3561	0.2984
	0.7637	0.1433	0.300	0.2491

过负荷	0.5687	0.0771	0.1406	0.1130
	0.5003	0.1565	0.2836	0.2606
	0.5732	0.0873	0.1605	0.0893
	0.2677	0.0450	0.0725	0.0339

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表 5 不同特征提取算法磨机负荷识别结果

特征提取算法	球磨机不同负荷状态识别率			总体识别率/%
特征提取算法	欠负荷识别率/%	正常负荷识别率/%	过负荷识别率/%	总体识别率/%
EEMD-样本熵	86.7	83.3	83.3	84.4
MEEMD-样本熵	90	86.7	86.7	87.8
CEEMDAN-样本熵	96.46	95.14	98.85	96.81

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