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

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

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.

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,timefrequency 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.