化工学报 ›› 2020, Vol. 71 ›› Issue (3): 1264-1277.doi: 10.11949/0438-1157.20190811

• 过程系统工程 • 上一篇    下一篇

基于改进EWT-多尺度熵和KELM的球磨机负荷识别方法

罗小燕(),戴聪聪,程铁栋,蔡改贫,刘鑫,刘吉顺   

  1. 江西理工大学机电工程学院,江西 赣州 341000
  • 收稿日期:2019-07-12 修回日期:2019-08-19 出版日期:2020-03-05 发布日期:2019-11-28
  • 通讯作者: 罗小燕 E-mail:978090634@qq.com
  • 基金资助:
    国家自然科学基金资助项目(51464017);江西省教育厅科技重点项目(GJJ150618)

Load identification method of ball mill based on improved EWT multi-scale entropy and KELM

Xiaoyan LUO(),Congcong DAI,Tiedong CHENG,Gaipin CAI,Xin LIU,Jishun LIU   

  1. School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
  • Received:2019-07-12 Revised:2019-08-19 Online:2020-03-05 Published:2019-11-28
  • Contact: Xiaoyan LUO E-mail:978090634@qq.com

摘要:

针对球磨机在磨矿过程中负荷靠经验难以准确判断的问题,提出了一种基于改进的经验小波变换(empirical wavelet transform, EWT)-多尺度熵和核极限学习机(KELM)的球磨机负荷识别方法。首先,针对筒体振动信号的多样性和复杂性特点,对EWT频谱分割方法进行改进,通过构建信号仿真模型,比较EWT、EMD的分解效果,证明该方法的有效性。再将不同负荷状态下的筒体振动信号用改进的EWT算法进行分解得到内禀模态函数(intrinsic mode function, IMF),接着,对分解后的IMF分量进行相关性分析得到敏感分量进行重构;最后,将重构信号的多尺度熵作为表征磨机不同负荷状态的特征向量,并计算多尺度熵偏均值。结果表明:三种负荷信号的多尺度熵及多尺度熵偏均值都存在明显的差异,关系表现为:欠负荷>正常负荷>过负荷。将提取的多维特征向量进行归一化处理并作为KELM的输入,磨机负荷状态作为输出,利用核排列(kernel target alignment, KTA)算法优化核参数,建立磨机负荷状态识别最优模型;通过磨矿实验验证了方法的可行性,相比SVM整体识别率提高了3.4%,且对于EMD-多尺度熵、EWT-多尺度熵分别提高了12.3%、8.9%。

关键词: 磨机负荷, 经验小波变换, 优化, KELM, 计算机模拟, 模型预测控制

Abstract:

Aiming at the problem that the load of the ball mill is difficult to accurately judge during the grinding process, a ball mill load identification method based on improved empirical wavelet transform (EWT)-multi-scale entropy and kernel extreme learning machine (KELM) is proposed. Firstly, according to the diversity and complexity of cylinder vibration signal, the EWT spectrum segmentation method is improved. By constructing the signal simulation model, the decomposition effect of EWT, EMD is compared, and the effectiveness of the method is proved. Secondly, the intrinsic modal function (IMF) is obtained by using the improved EWT algorithm to decompose the vibration signal of the cylinder under different load states, and then the effective IMF component is selected for reconstruction by correlation analysis. Finally, the multi-scale entropy of the reconstructed signal is extracted as the eigenvector to characterize the different load states of the mill, and the mean value of multi-scale entropy deviation is calculated. The results show that there are obvious differences in the mean value of multi-scale entropy and multi-scale entropy of the three load signals, and the relationship between the three load signals is underload > normal load > overload. The extracted multidimensional feature vector is normalized and used as the input of KELM and the load state of the mill is used as the output. The kernel target alignment (KTA) algorithm is used to optimize the kernel parameters and the optimal model of mill load state identification is established. The feasibility of the method is verified by grinding experiments. Compared with SVM, the overall recognition rate of EWT- is 3.4% higher, and for EMD-multi-scale entropy, EWT-multi-scale entropy is increased by 12.3% and 8.9%, respectively.

Key words: mill load, EWT, optimal, KELM, computer simulation, model-predictive control

中图分类号: 

  • TP 29

图1

KTA-KELM磨机负荷状态识别模型建立流程图"

图2

仿真信号x(t)的时域波形"

图3

改进EWT频谱分割图及相应的小波滤波器组"

图4

传统EWT的频谱分割图及相应的小波滤波器组"

图5

改进EWT和传统EWT分解结果(红色虚线代表原始信号,蓝色实线代表分解结果)"

图6

EMD分解结果"

图7

实验装置"

图8

原始信号"

图9

IMF分量与原始信号的相关系数"

图10

重构信号"

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9.2326.68
正常负荷10.7824.37
过负荷9.1127.41

图11

欠负荷筒体振动信号重构"

表2

不同算法去噪后的信噪比"

算法信噪比SNR/dB
EMD12.65
EWT18.47
改进EWT26.68

表3

3种负荷状态振动信号的样本熵值"

数据样本欠负荷正常负荷过负荷
A10.10310.06630.0442
A20.10170.05540.0561
A30.09180.05350.0473
A40.10030.06640.0531
A50.09740.06310.0514
均值0.098860.06090.05042

图12

不同负荷下振动信号多尺度熵随不同数据长度N变化"

图13

不同负荷下振动信号多尺度熵随相似容限r变化"

图14

不同算法下三种负荷状态重构信号的多尺度熵值变化"

图15

不同算法下三种负荷状态重构信号的多尺度熵偏均值"

图16

KTA-KELM和KELM算法稳定性对比"

图17

负荷识别结果"

表4

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/%
欠负荷正常负荷过负荷
EMD-多尺度熵86.783.383.384.4
EWT-多尺度熵9086.786.787.8
改进EWT-多尺度熵10093.396.796.7
1 蔡改贫,宗路,罗小燕,等.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 G P,Zong L,Luo X Y,et al.Research on mill load forecasting based on CEMDAN-cloud model characteristic entropy and LSSVM[J].Vibration and Impact,2019,38(7):128-133.
2 杜永贵,李思思,阎高伟,等.基于流形正则化域适应湿式球磨机负荷参数软测量[J].化工学报,2018,69(3):1244-1251.
Du Y G,Li S S,Yan G W,et al.Soft measurement of load parameters of wet ball mill based on Manifold Regularization domain[J].CIESC Journal,2018,69(3):1244-1251.
3 白锐,柴天佑.基于数据融合与案例推理的球磨机负荷优化控制[J].化工学报,2009,60(7):1746-1752.
Bai R,Chai T Y.Ball mill load optimization control based on data fusion and case-based reasoning[J].CIESC Journal,2009,60(7):1746-1752.
4 胡显能,蔡改贫,罗小燕,等.基于CEEMDAN和多尺度排列熵的球磨机负荷识别方法[J].噪声与振动控制,2018,38(3):146-151.
Hu X N,Cai G P,Luo X Y,et al.Load identification method of ball mill based on CEMDAN and multi-scale permutation entropy[J].Noise and Vibration Control,2018,38(3):146-151.
5 阎高伟,龚杏雄,续欣莹.基于云模型的球磨机料位概念表示与测量模型[J].中国电机工程学报,2014,34(14):2281-2288.
Yan G W,Gong X X,Xu X Y.Conceptual representation and measurement model of ball mill level based on cloud model[J].Chinese Journal of Electrical Engineering,2014,34(14):2281-2288.
6 汤健,柴天佑,丛秋梅,等.选择性融合多尺度筒体振动频谱的磨机负荷参数建模[J].控制理论与应用,2015,32(12):1582-1591.
Tang J,Chai T Y,Cong Q M,et al.Modeling of mill load parameters based on selective fusion of multi-scale cylinder vibration spectrum[J].Control Theory and Application,2015,32(12):1582-1591.
7 赵立杰,柴天佑,汤健,等.基于EMD和选择性集成学习算法的磨机负荷参数软测量[J].自动化学报,2014,40(9):1853-1866.
Zhao L J,Chai T Y,Tang J,et al.Soft measurement of mill load parameters based on EMD and selective integrated learning algorithm[J].Journal of Automation,2014,40(9):1853-1866.
8 蔡改贫,宗路,刘鑫,等.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.
Cai G P,Zong L,Liu X,et al.Ball mill load identification method based on MEEMD-multiscale fractal box dimension and ELM[J].CIESC Journal,2019,70(2):764-771.
9 罗小燕,卢小江,熊洋,等.小波分析球磨机轴承振动信号特征提取方法[J].噪声与振动控制,2016,36(1):148-152.
Luo X Y,Lu X J,Xiong Y,et al.Wavelet analysis method for feature extraction of bearing vibration signal of ball mill[J].Noise and Vibration Control,2016,36(1):148-152.
10 Gilles J.Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
11 Huimin Z,Shaoyan Z,Jian F,et al.Study on a motor bearing fault diagnosis method using improved EWT based on scale space threshold method[J].International Journal of Emerging Electric Power Systems,2018,19(4):1-13.
12 肖启阳,李健,孙洁娣,等.基于EWT及模糊相关分类器的管道微小泄漏检测[J].振动与冲击,2018,37(14):122-129.
Xiao Q Y,Li J,Sun J D,et al.Pipeline micro-leakage detection based on EWT and fuzzy correlation classifier[J].Vibration and Impact,2018,37(14):122-129.
13 He Y,Hongru L,Yaolong L,et al.A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps[J].Measurement,2018,133(1):145-161.
14 祝文颖,冯志鹏.基于改进经验小波变换的行星齿轮箱故障诊断[J].仪器仪表学报,2016,37(10):2193-2201.
Zhu W Y,Feng Z P.Fault diagnosis of planetary gearbox based on improved empirical wavelet transform[J].Chinese Journal of Science Instrument,2016,37(10):2193-2201.
15 辛玉,李舜酩,王金瑞,等.基于迭代经验小波变换的齿轮故障诊断方法[J].仪器仪表学报,2018,39(11):79-86.
Xin Y,Li S Q,Wang J R,et al.Gear fault diagnosis method based on iterative empirical wavelet transform[J].Chinese Journal of Science Instrument,2018,39(11):79-86.
16 Merainani B,Rahmoune C,Djamel B,et al.A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network[J].Journal of Vibration and Control,2018,24(12):2512-2531.
17 高正.基于EWT和特征融合的钻机钻杆故障识别研究[D].杭州:浙江大学,2019.
Gao Z.Research on drill pipe fault identification based on EWT and feature fusion [D].Hangzhou:Zhejiang University,2019.
18 He Q,Wang Y W,Du S,et al.Motor imagery based on adaptive parameter less empirical wavelet transform and selective integrated classification[J].Acta Physica Sinica,2018,67(11):185-196.
19 Costa M,Goldberger A L,Peng C K.Multiscale entropy analysis of complex physiologic time series[J].Physical Review Letters,2002,89(6):1-18.
20 Begum S,Barua S,Filla R.Classification of physiological signals for wheel loader operators using multi-scale entropy analysis and case-based reasoning[J].Expert Systems with Application,2014,41(2):295-305.
21 Liu Z,Chai T Y,Yu W,et al.Multi-frequency signal modeling using empirical mode decomposition and PCA with application to mill load estimation[J].Neurocomputing,2015,69(23):392-402.
22 苟先太,李昌喜,金炜东.VMD多尺度熵用于高速列车横向减振器故障诊断[J].振动.测试与诊断,2019,39(2):292-297+442.
Gou X T,Li C X,Jin W D.VMD multiscale entropy for fault diagnosis of lateral shock absorber of high speed train[J].Vibration. Testing and Diagnosis,2019,39(2):292-297+442.
23 李从志,郑近德,潘海洋,等.基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断方法[J/OL].中国机械工程[2019-07-09].
Li C Z,Zheng J D,Pan H Y,et al.Rolling bearing fault diagnosis method based on fine composite multiscale dispersion entropy and support vector machine[J/OL].China Mechanical Engineering: [2019-07-09].
24 Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems,Man, andCybernetics-Part B: Cybernetics,2012,42(2):513-529.
25 范永东.模型选择中的交叉验证方法综述[D].太原:山西大学,2013.
Fan Y D.Overview of cross-validation methods in model selection[D].Taiyuan:Shanxi University,2013.
26 章勇高,高彦丽,马迪.基于GA-KELM的光伏短期出力预测研究[J].控制工程,2018,25(7):1155-1159.
Zhang Y G,Gao Y L,Ma D.Study on short-term photovoltaic output prediction based on GA-KELM[J].Control Engineering,2018,25(7):1155-1159.
27 张文涛,马永光,董子健,等.基于粒子群算法优化核极限学习机的磨煤机故障诊断[J].电力科学与工程,2018,34(9):54-58.
Zhang W T,Ma Y G,Dong Z J,et al.Fault diagnosis of coal mill based on particle swarm optimization for nuclear limit learning machine[J].Electric Power Science and Engineering,2018,34(9):54-58.
28 王裴岩,蔡东风.一种基于核距离的核函数度量方法[J].计算机科学,2014,41(2):72-75.
Wang P Y,Cai D F.A kernel function measurement method based on kernel distance[J].Computer Science,2014,41(2):72-75.
29 王建国,杨柳,张文兴.核排列优化的支持向量机在齿轮故障诊断中的应用[J].机械设计与制造,2018, (S1):37-40.
Wang J G,Yang L,Zhang W X.Application of support vector machine with kernel arrangement optimization in gear fault diagnosis[J].Mechanical Design and Manufacture,2018, (S1):37-40.
30 Albert A P,Nii A O.A criterion for selecting relevant intrinsic mode functions in empirical mode decommposition[J].Advances in Adaptive Data Analysis,2010,2(1):1-24.
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[2] 罗向龙, 华贲. 蒸汽动力系统设计与综合优化研究综述 [J]. 化工学报, 2009, 60(10): 2411 -2419 .
[3] 吴承旭, 周健, 李雪飞, 李磊. PBT/PC合金塑料的性能与微观结构 [J]. 化工学报, 2010, 61(6): 1571 -1576 .
[4] 郑一舟,丁新华,岑沛霖,刘恒. 乳酸的固定化细胞发酵及其在聚乙烯基吡啶树脂上的吸附研究 [J]. CIESC Journal, 1992, 43(3): 317 -322 .
[5] 朱建华,廖晖,李绍芬. 在铂催化剂上-氧化碳氧化反应的多定态特性 [J]. CIESC Journal, 1992, 43(5): 515 -522 .
[6] 张正江, 邵之江. 基于有限测量信息的过程系统参数可估计性分析 [J]. 化工学报, 2011, 62(2): 433 -438 .
[7] 何琼;赵刚 .

人体血液灌注和代谢产热对低温手术过程的影响

[J]. CIESC Journal, 2006, 57(5): 1127 -1132 .
[8] 徐铜文,范文元. 乳状液膜法提取钪的反应扩散模型 [J]. CIESC Journal, 1994, 45(1): 88 -93 .
[9] 李清彪,陈洪钫. 改进的扩散自由体积模型及其在凝胶和固定化细胞中的应用(Ⅰ)模型的建立 [J]. CIESC Journal, 1994, 45(4): 435 -440 .
[10] 李平, 李奇安, 雷荣孝, 陈爱军, 任丽丽, 曹巍. 乙烯裂解炉先进控制系统开发与应用 [J]. 化工学报, 2011, 62(8): 2216 -2220 .