基于邻域保持嵌入的主多项式非线性过程故障检测
收稿日期: 2020-09-14
网络出版日期: 2021-06-08
基金资助
国家自然科学基金重大项目(61490701);国家自然科学基金项目(61673279)
Principal Polynomial Nonlinear Process Fault Detection Based on Neighborhood Preserving Embedding
Received date: 2020-09-14
Online published: 2021-06-08
针对化工过程的变量数据维数高、非线性的问题,提出基于邻域保持嵌入(NPE)-主多项式分析(PPA) 的过程故障检测算法.应用NPE算法提取高维数据的低维子流形,能够解决传统的线性降维算法不能提取局部结构信息的问题,对维数进行约减.利用PPA法时,使用一组灵活的主多项式分量来描述数据, 能够有效地捕捉过程数据中固有的非线性结构.在降维后的流形空间进行主多项式分析并建立Hotelling’s T2和平方预测误差统计量模型,同时确定控制限以进行故障检测.最后,通过一组非线性数值实例和Tennessee Eastman化工过程数据,将NPE-PPA算法与传统的核主元分析法、PPA法进行对比分析,验证所提算法的有效性及优越性.
李元, 姚宗禹 . 基于邻域保持嵌入的主多项式非线性过程故障检测[J]. 上海交通大学学报, 2021 , 55(8) : 1001 -1008 . DOI: 10.16183/j.cnki.jsjtu.2020.295
Aimed at the problem of high dimension and nonlinearity of variable data in chemical process, a process fault detection algorithm based on neighborhood preserving embedding(NPE )-principal polynomial analysis (PPA) is proposed in this paper. The NPE algorithm is used to extract low dimensional submanifolds of high dimensional data, which overcomes the problem that the traditional linear dimensionality reduction algorithm cannot extract local structure information, so as to reduce the dimensions. The PPA method is used to describe data by a set of flexible principal polynomial components, which can effectively capture the inherent nonlinear structure of process data. The principal polynomial analysis is conducted in the reduced manifold space, and Hotelling’s T2 and square prediction error statistical models are established to determine the control limit for fault detection. Finally, compared with the traditional kernel principal component analysis and the PPA method, a group of nonlinear numerical examples and Tennessee Eastman chemical process data experiments are performed to verify the effectiveness and superiority of the NPE-PPA algorithm.
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