基于EMD和选择性集成学习算法的磨机负荷参数软测量

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.

Soft Sensor Approach for Modeling Mill Load Parameters Based on EMD and Selective Ensemble Learning Algorithm

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.