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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
Alternative TitleSoft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm
汤健; 柴天佑; 丛秋梅; 苑明哲; 赵立杰; 刘卓; 余文
Department信息服务与智能控制技术研究室
Source Publication自动化学报
ISSN0254-4156
2014
Volume40Issue:9Pages:1853-1866
Indexed ByEI ; CSCD
EI Accession number20144200113083
CSCD IDCSCD:5242911
Contribution Rank3
Funding Organization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
Keyword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
Abstract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...
Other AbstractThe 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.
Language中文
Citation statistics
Cited Times:20[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15189
Collection信息服务与智能控制技术研究室
Affiliation1.中国人民解放军92941部队
2.东北大学自动化研究中心
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
4.沈阳化工大学信息工程学院
5.墨西哥国立理工大学高级研究中心
Recommended Citation
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.
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