基于多模态特征子集选择性集成建模的磨机负荷参数预测方法

刘卓; 汤健; 柴天佑; 余文

doi:10.16383/j.aas.c190735

基于多模态特征子集选择性集成建模的磨机负荷参数预测方法

doi: 10.16383/j.aas.c190735

刘卓^1,,
汤健^2,,
柴天佑^1,,
余文^3,

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819 中国
2.
北京工业大学信息学部北京 100124 中国
3.
墨西哥国立理工大学高级研究中心(CINVESTAV-IPN) 墨西哥 07360 墨西哥

基金项目: 国家自然科学基金(61703089, 61803191, 61673097), 中央高校基本科研业务费专项资金项目(N17080400), 矿冶过程自动控制技术国家重点实验室, 矿冶过程自动控制技术北京市重点实验室(BGRIMM-KZSKL-2020-02)资助

详细信息

作者简介:
刘卓：东北大学流程工业综合自动化国家重点实验室讲师. 主要研究方向为复杂工业过程建模. E-mail: liuzhuo@ise.neu.edu.cn

汤健：北京工业大学教授. 主要研究方向为小样本数据建模, 城市固废自动化处理. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

柴天佑：中国工程院院士, 东北大学教授. IEEE Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化理论、方法与技术. E-mail: tychai@mail.neu.edu.cn

余文：墨西哥国立理工大学高级研究中心自动化部教授. 主要研究方向为复杂工业过程建模与控制, 机器学习. E-mail: yuw@ctrl.cinvestav.mx

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出版历程
- 收稿日期: 2019-10-23
- 录用日期: 2020-02-07
- 网络出版日期: 2021-08-25
- 刊出日期: 2021-08-20

Selective Ensemble Modeling Approach for Mill Load Parameter Forecasting Based on Multi-modal Feature Sub-sets

LIU Zhuo^1
,,
TANG Jian^2
,,
CHAI Tian-You^1
,,
YU Wen^3
,

1.
State Key Laboratory of Synthetical Automation for Process Industries, North-eastern University, Shenyang 110819, China
2.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
3.
Departamento de Control Automatico, Centro de Investigation de Estudios Avanzados, National Polytechnic Institute Mexico, México D. F. 07360, Mexico

Funds: Supported by National Natural Science Foundation of China (61703089, 61803191, 61673097), the Fundamental Research Funds for the Central Universities (N17080400), National & Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2020-02)

More Information

Author Bio:
LIU Zhuo　Lecturer at the State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University. Her research interest covers modeling for complex industries

TANG Jian　Professor at Beijing University of Technology. His research interest covers small sample data modeling and intelligent automatic processing of municipalsolid. Corresponding author of this paper

CHAI Tian-You　Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Fellow, IFAC Fellow, and Academician of the International Eurasian Academy of Sciences. His research interest covers adaptive control, intelligent decoupling control, and integrated automation thoery, method and technology of industrial process

YU Wen　Professor in the Departamento de Control Automatico of the Centro de Investigation de Estudios Avanzados, National Polytechnic Institute Mexico. His research interest covers modeling and control of the complex industrial process, and machine learning

摘要

摘要:
如何融合球磨机系统研磨过程所产生的多模态机械信号构建磨机负荷参数预测(Mill load parameter forecasting, MLPF)模型是当前研究的热点. 针对上述问题, 本文提出一种基于多模态特征子集选择性集成(Selective ensemble, SEN)建模的MLPF方法. 首先, 对多模态机械信号进行时频域变换得到高维频谱数据; 接着, 采用相关系数法和互信息法对多模态频谱进行线性和非线性特征子集的自适应选择; 最后, 采用优化和加权算法对上述特征子集的候选子模型进行自适应地选择与合并, 得到基于SEN机制的MLPF模型. 采用磨矿过程实验球磨机的机械信号仿真验证了所提方法的有效性.
- 多模态机械信号 /
- 特征子集 /
- 选择性集成 /
- 磨机负荷参数
Abstract:
How to fuse the multi-modal mechanical signals of the ball mill grinding system to construct mill load parameter forecasting (MLPF) model is a hot issue in current research. Aiming at the above problem, a new selective ensemble (SEN) modeling approach for MLPF based on multi-modal feature sub-sets is proposed. Firstly, time-frequency domain transformation is performed on the multi-modal mechanical signals to obtain high-dimensional spectral data; Then, linear and non-linear feature subsets are adaptively selected from multi-modal frequency spectrum by using feature selection approaches based on correlation coefficient and mutual information method. Finally, linear and non-linear candidate sub-models based on different multi-modal feature sub-sets are constructed, which are adaptively selected and combined by using optimal selection and weighting algorithm jointly. Thus, the final SEN model for MLPF is obtained. Simulation results based on mechanical signals of a laboratory scale ball mill showed the effectiveness of the proposed method.
- Multi-modal mechanical signal /
- feature sub-set /
- selective ensemble modeling /
- mill load parameter

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图 1 磨机系统不同位置机械信号的产生机理示意图

Fig. 1 Generation mechanism of mechanical signals in different position of mill system

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图 2 建模策略

Fig. 2 The proposed modeling strategy

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图 3 实验球磨机传感器布置示意图

Fig. 3 Layout of sensors for experimental ball mill

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图 4 模态Ch1, Ch6, Ch8的频谱变量与MBVR间的相关系数和互信息值

Fig. 4 Correlation coefficient and mutual information value between spectrum variable of mode Ch1, Ch6, Ch8 and MBVR

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表 1 面向PD的不同模态频谱特征的特征选择系数统计表

Table 1 Coefficients statistical table of different modal spectrum feature for PD

类别	Ch1	Ch2	Ch3	Ch4	Ch5	Ch6	Ch7	Ch8
线性特征选择系数Min	0.09050	0.007868	0.3678	0.005018	0.0001994	0.009596	0.002075	0.8741
线性特征选择系数Max	1.2897	1.7351	1.1913	1.3904	5.2883	1.2649	2.0564	1.0571
非线性特征选择系数Min	0.6644	0.5659	0.8813	0.8403	0.5718	0.7039	0.4860	0.9228
非线性特征选择系数Max	1.0715	1.0885	1.1680	1.1304	1.3556	1.1352	1.623	1.0599

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表 2 候选子模型编码

Table 2 Coding of candidate sub-models

序号	子模型特点	子模型名称	子模型编码	多模态通道编号
1	lin_lin	Corr-PLS	1-8	1-Ch1, 2-Ch2, 3-Ch3, 4-Ch4, 5-Ch5, 6-Ch6, 7-Ch7, 8-Ch8
2	nonlin_lin	Mi-PLS	9-16	9-Ch1, 10-Ch2, 11-Ch3, 12-Ch4, 13-Ch5, 14-Ch6, 15-Ch7, 16-Ch8
3	lin_nonlin	Corr-RWNN	17-24	17-Ch1, 18-Ch2, 19-Ch3, 20-Ch4, 21-Ch5, 22-Ch6, 23-Ch7, 24-Ch8
4	nonlin_nonlin	Mi-RWNN	25-32	25-Ch1, 26-Ch2, 27-Ch3, 28-Ch4, 29-Ch5, 30-Ch6, 31-Ch7, 32-Ch8

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表 3 不同特征选择系数时所构建的SEN模型的预测误差和所选择的子模型编号

Table 3 Prediction error of SEN model with different feature selection coefficients and selected sub-model number

序号	MBVR		PD		CVR
序号	测试误差	集成子模型编号	测试误差	集成子模型编号	测试误差	集成子模型编号
1	0.05330	{ 21 23 27 31 17 32 19 24 30}	0.01579	{26 18 30}	0.01083	{14 19 26 18 30 22}
2	0.06204	{14 31 32 24 27 30}	0.01805	{25 10 31 32 14 19 24 18 30}	0.009697	{27 26 22 30}
3	0.04515	{9 17 26 14 30 27 22 32 19 24}	0.01855	{24 14 18 30 26}	0.01146	{27 14 19 26 31 18 30 22}
4	0.04717	{23 17 27 19 32 24 30}	0.01582	{14 24 26 27 32 30}	0.009544	{19 30 22}
5	0.05231	{27 17 30 23 19 32 24}	0.01843	{24 14 25 22 18 19 30}	0.01093	{20 14 31 27 32 19 26 22 30}
6	0.04433	{31 22 30 32 19 24}	0.01452	{22 14 24 32 26 19 30}	0.009930	{23 25 20 18 32 27 26 19 30 22}
7	0.05697	{31 32 24}	0.01627	{26 22 18 24 32 19 30}	0.009870	{6 20 28 19 32 18 26 27 22 30}
8	0.04459	{27 26 23 22 31 25 30 17 32 24}	0.01687	{27 18 32 19 30}	0.009280	{28 18 26 27 19 22 30}
9	0.04969	{26 32 27 30 25 19 24}	0.01718	{2 18 27 6 26 32 25 30}	0.009650	{18 32 26 25 27 19 30 22}
10	0.04624	{22 17 26 27 30 25 32 19 31 24}	0.01748	{25 26 22 32 27 6 18 19 30}	0.01212	{22 30}
11	0.04404	{25 17 18 27 22 19 30 24}	0.01769	{17 23 22 26 27 6 30 19 18}	—	—

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表 4 磨机负荷参数各通道与多模态特征子集选择性集成模型的测试误差比较

Table 4 Comparison of test errors between various channels of mill load parameters and multi-modal feature subset SEN model

	RMSREs												备注
	MBVR				PD				CVR
	Corr-PLS	Mi-PLS	Corr-RWNN	Mi-RWNN	Corr-PLS	Mi-PLS	Corr-RWNN	Mi-RWNN	Corr-PLS	Mi-PLS	Corr-RWNN	Mi-RWNN
Ch1	0.1924	0.3426	0.1314	0.1503	0.06710	0.05411	0.06910	0.05161	0.05911	0.06622	0.07030	0.04930	筒体振动
Ch2	0.3213	0.7207	0.3103	0.1401	0.04221	0.04430	0.03321	0.03751	0.05650	0.04711	0.03711	0.02620	筒体振动
Ch3	0.4401	0.4431	0.09112	0.09020	0.12012	0.07611	0.03111	0.05210	0.1132	0.07831	0.02922	0.03810	筒体振声
Ch4	0.5125	0.4225	0.2822	0.2001	0.1142	0.08620	0.06460	0.1184	0.07442	0.06910	0.04110	0.04772	筒体振声
Ch5	0.4611	0.3409	0.1911	0.2221	0.1087	0.08122	0.1161	0.09810	0.09711	0.09610	0.04440	0.09911	轴承振动
Ch6	0.3105	0.2141	0.1431	0.1341	0.04410	0.03720	0.03520	0.02431	0.03520	0.03641	0.01630	0.01720	轴承振动
Ch7	0.3802	0.2502	0.1321	0.1101	0.1083	0.06241	0.06121	0.05611	0.09451	0.04811	0.04910	0.04141	轴承振动
Ch8	0.5934	0.6031	0.08090	0.3631	0.0971	0.07910	0.03310	0.03220	0.1421	0.08930	0.06840	0.03730	研磨振声
本文方法	0.04404				0.01452				0.00928

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表 5 磨机负荷参数各通道与多模态特征子集选择性集成模型的平均测试误差比较

Table 5 Average test errors comparison of the various channels of mill load parameters and multi-modal feature subset SEN model

通道	MBVR	PD	CVR	平均预测误差	备注
Ch1	0.1314	0.05161	0.04930	0.07740	筒体振动
Ch2	0.1401	0.03321	0.02620	0.06650	筒体振动
Ch3	0.09020	0.03111	0.02922	0.05020	筒体振声
Ch4	0.2001	0.06460	0.04110	0.1019	筒体振声
Ch5	0.1911	0.08122	0.04440	0.1056	轴承振动
Ch6	0.1341	0.02431	0.01630	0.05820	轴承振动
Ch7	0.1101	0.05611	0.04141	0.06920	轴承振动
Ch8	0.08090	0.03220	0.03730	0.05010	研磨振声
本文方法	0.04404	0.01452	0.00928	0.02260

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