中国科学院机构知识库网格系统: 基于EMD和选择性集成学习算法的磨机负荷参数软测量

基于EMD和选择性集成学习算法的磨机负荷参数软测量

文献类型：期刊论文


作者	汤健; 柴天佑; 丛秋梅; 苑明哲; 赵立杰; 刘卓; 余文
刊名	自动化学报
出版日期	2014
卷号	40 期号:9 页码:1853-1866
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
ISSN号	0254-4156
其他题名	Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm
产权排序	3
中文摘要	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...
英文摘要	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 difficult 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.
收录类别	EI ; CSCD
资助信息	国家自然科学基金(61034008,61004051,61203102,61020106003,61134006);111计划(B08015);国家支撑计划(2012-BAF19G00);中国博士后科学基金(2013M532118,2013M530953,2013M541820)资助
语种	中文
CSCD记录号	CSCD:5242911
公开日期	2014-11-03
源URL	[http://ir.sia.cn/handle/173321/15189]
专题	沈阳自动化研究所_信息服务与智能控制技术研究室
推荐引用方式 GB/T 7714	汤健,柴天佑,丛秋梅,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.
APA	汤健.,柴天佑.,丛秋梅.,苑明哲.,赵立杰.,...&余文.(2014).基于EMD和选择性集成学习算法的磨机负荷参数软测量.自动化学报,40(9),1853-1866.
MLA	汤健,et al."基于EMD和选择性集成学习算法的磨机负荷参数软测量".自动化学报 40.9(2014):1853-1866.

入库方式： OAI收割

来源：沈阳自动化研究所

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基于EMD和选择性集成学习算法的磨机负荷参数软测量

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