基于最大均值差异的多模态过程过渡模态识别方法

Transition mode identification method based on maximum mean discrepancy for multimode process

为了更好地揭示多模态过程的运行状态和数据分布变化规律,提高后续建模精度,提出基于最大均值差异(MMD)的多模态过程的过渡模态识别方法.引入滑动窗口对数据进行切割,使用最大均值差异对多模态过程数据的分布差异进行度量,通过与稳定模态阈值α比较区分过程数据的稳定模态和过渡模态.在过渡模态段内减小滑动窗口窗宽,利用过渡模态阈值β识别出过渡子模态.数值仿真实验的模态识别结果表明,所提方法可以准确检测出输入变量期望值的阶跃变化,实现对模态的准确识别.田纳西伊斯曼(TE)过程仿真数据实验表明,所提方法可以有效地划分出合理的模态,进而选择出分布最相近的历史模态建模,提高多模态过程的软测量建模精度.

A transition mode identification method based on maximum mean discrepancy(MMD)was proposed in order to better reveal the law of the operating state and the process data distributional variation of a multi-mode process,and further improve the accuracy of subsequent modeling.Firstly,the sliding window technique was introduced to segment process data,and the MMD was used to measure the distribution difference of the process data.The stable mode and transition mode of the process data were distinguished by comparing the MMD with the stable modal thresholdα.Secondly,the width of the sliding window was reduced in transition mode segments,and a transition mode thresholdβwas used to identify the transition modes.The modal identification results of numerical simulation experiments show that the proposed method can achieve the goal of detecting the step change of the expected value of the input variables and identifying the transient modes.Tennessee Eastman(TE)process simulation data experiments show that the proposed method can effectively divide reasonable modes,select the historical mode modeling with the closest distribution,and improve the soft sensor modeling accuracy of multimodal processes.