一种基于多工况识别的过程在线监测方法

A process on-line monitoring method based on multi-mode identification

在包含多个工况的工业生产过程中,各个稳态工况之间存在着一定的过渡过程,虽然过渡时间较短,但其复杂的动态特性使得传统的过程监测方法难以获得满意的效果,为此提出一种基于多工况识别的过程监测方法.首先,通过窗口切割对基本稳态工况进行识别;然后,采用滑动窗技术确定过渡过程的起始和结束时间,并进一步基于差分分段技术对过渡过程的子阶段进行分类,考虑到各阶段数据的不同分布特性,利用独立成分分析和主元分析分别提取各阶段数据的非高斯和高斯信息;最后根据贝叶斯推断将3个统计量进行重构,实现多工况过程的在线监测.通过TE过程的仿真研究,验证了所提出方法的可行性和有效性.

In the industrial production process containing a number of different modes, there is a certain transition process between every two steady modes. Although the transition period is rather short, its the complex dynamic behavior makes traditional process monitoring methods difficult to obtain satisfactory results. Therefore, a process monitoring method based on multi-mode identification is proposed. Firstly, the identification of the basic steady modes is realized through a window cutting method. Then, the accurate time boundaries of transitional modes are determined by using a moving window strategy, and a differential segmentation technique is performed on the sub-modes of the transitional process for classification. Considering different distribution characteristics of each data segment, independent component analysis and principal component analysis are carried out to deal with the non-Gaussian and Gaussian information. Finally,according to the Bayesian inference, three statistics are reconstructed to realize the multi-mode process monitoring. The feasibility and effectiveness of the proposed method are later demonstrated through a simulated Tennessee Eastman（TE）process.