基于EMD和选择性集成学习算法的磨机负荷参数软测量
Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm
查看参考文献25篇
|
文摘
|
针对磨机筒体振动和振声信号组成复杂难以解释、蕴含信息存在冗余性和互补性、与磨机负荷参数映射关系难以描述等问题,提出了基于经验模态分解(Empirical mode decomposition, EMD)技术和选择性集成学习算法分析筒体振动与振声信号组成,建立磨机负荷参数软测量模型的新方法。首先从机理上定性分析了筒体振动及振声信号组成的复杂性;然后采用EMD技术将原始信号自适应分解为具有不同时间尺度的系列组成成分,即本征模态函数(Intrinsic mode function, IMF) ;接着在频域内基于互信息(Mutual information, MI)方法分析并选择IMF 频谱特征;最后采用基于核偏最小二乘(Kernel partial least square, KPLS)建模方法、分支定界优化算法的选择性集成学习方法建立磨机负荷参数软测量模型,实现了多源多尺度频谱特征的选择性信息融合。基于实验球磨机的实际运行数据仿真验证了该方法的有效性。 |
|
其他语种文摘
|
The components of shell vibration and acoustical signals of ball mill are complexity and difficult to interpret. Moreover, the useful information contained in these signals is redundancy and complementary, and the mapping relationships between these signals and mill load parameters are difficult to describe. Aiming at these problems, a new soft sensor approach is proposed, which analyzes shell vibration and acoustical signals for modeling mill load parameters based on empirical mode decomposition (EMD) technology and selective ensemble learning algorithm. At first, the complexity of the shell vibration and acoustical signals are analyzed based on the production mechanism. Then, these signals are adaptive decomposed into a number of intrinsic mode functions (IMFs) with different time-scales using EMD technology, and the spectral features of IMFs are analyzed and selected based on the mutual information (MI) method. At last, the selective ensemble learning algorithm based on kernel partial least square modeling approach and the brand and bound optimal algorithm are used to construct soft sensor models of mill load parameters. Thus, the selective information fusion based on multi-source frequency spectrum features is realized. The simulation results based on operating data from the laboratory ball mill validate the proposed approach. |
|
来源
|
自动化学报
,2014,40(9):1853-1866 【核心库】
|
|
DOI
|
10.3724/sp.j.1004.2014.01853
|
|
关键词
|
经验模态分解
;
选择性集成建模
;
磨机负荷参数
;
选择性信息融合
;
频谱特征
|
|
地址
|
1.
中国人民解放军92941部队, 葫芦岛, 125001
2.
东北大学自动化研究中心, 沈阳, 110004
3.
中国科学院沈阳自动化研究所信息服务与智能控制技术研究室, 沈阳, 110016
4.
沈阳化工大学信息工程学院, 沈阳, 110142
5.
墨西哥国立理工大学高级研究中心, 墨西哥, 07360
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0254-4156 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
;
国家教育部高等学校学科创新引智计划项目
;
国家科技支撑计划项目
;
中国博士后科学基金
|
|
文献收藏号
|
CSCD:5242911
|
参考文献 共
25
共2页
|
|
1.
Wei D H. Grinding mill circuits|a survey of control and economic concerns.
International Journal of Mineral Process,2009,90(1/4):56-66
|
被引
3
次
|
|
|
|
|
2.
柴天佑. 复杂工业过程运行优化与反馈控制.
自动化学报,2013,39(11):1744-1757
|
被引
52
次
|
|
|
|
|
3.
汤健. 磨机负荷检测方法研究综述.
控制工程,2010,17(5):565-570
|
被引
11
次
|
|
|
|
|
4.
Zhou P. Intelligent optimal-setting control for grinding circuits of mineral processing.
IEEE Transactions on Automation Science and Engineering,2009,6(4):730-743
|
被引
48
次
|
|
|
|
|
5.
Huang P. Investigation on measuring the flll level of an industrial ball mill based on the vibration characteristics of the mill shell.
Minerals Engineering,2009,14(22):1200-1208
|
被引
5
次
|
|
|
|
|
6.
Tang J. Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell.
Minerals Engineering,2010,23(9):720-730
|
被引
18
次
|
|
|
|
|
7.
Das S P. Interpretation of mill vibration signal via wireless sensing.
Minerals Engineering,2011,24(3/4):245-251
|
被引
6
次
|
|
|
|
|
8.
冯天晶. 基于筒壁振动信号的磨机工况检测系统.
选矿,2012,19(2):66-69
|
被引
2
次
|
|
|
|
|
9.
Tang J. Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process.
Control Engineering Practice,2012,20(10):991-1004
|
被引
10
次
|
|
|
|
|
10.
桂卫华. 有色冶金过程建模与优化的若干问题及挑战.
自动化学报,2013,39(3):197-207
|
被引
24
次
|
|
|
|
|
11.
汤健. 基于振动频谱的磨矿过程球磨机负荷参数集成建模方法.
控制理论与应用,2012,29(2):183-191
|
被引
13
次
|
|
|
|
|
12.
Tang J. Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information.
IEEE Transactions on Automation Science and Engineering,2013,10(3):726-740
|
被引
18
次
|
|
|
|
|
13.
郝红卫. 分类器的动态选择与循环集成方法.
自动化学报,2013,39(11):1290-1295
|
被引
1
次
|
|
|
|
|
14.
汤健. 基于振动频谱的磨机负荷在线软测量建模.
信息与控制,2012,41(1):123-128
|
被引
12
次
|
|
|
|
|
15.
汤健. 在线KPLS 建模方法及在磨机负荷参数集成建模中的应用.
自动化学报,2013,39(5):471-486
|
被引
12
次
|
|
|
|
|
16.
Huang N E. The mechanism for frequency downshift in nonlinear wave evolution.
Advances in Applied Mechanics,1996,32:59-117
|
被引
20
次
|
|
|
|
|
17.
Yang J N. System identiflcation of linear structure based on Hilbert-Huang spectral analysis. Part 1: Normal Modes.
Earthquake Engineering&Structure Dynamics,2003,32(9):1443-1467
|
被引
75
次
|
|
|
|
|
18.
Yan R Q. Rotary machine health diagnosis based on empirical mode decomposition.
Journal of Vibration an Acoustics,2008,130(2):1-12
|
被引
4
次
|
|
|
|
|
19.
Chen J. Application of empirical mode decomposition in structural health monitoring: some experience.
Advances in Adaptive Data Analysis,2009,1(4):601-621
|
被引
4
次
|
|
|
|
|
20.
Huang P. Bearing fault diagnosis based on EMD and PSD.
Proceedings of the 8th World Congress on Intelligent Control and Automation, WCICA 2010,2010:1300-1304
|
被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问