Principal Polynomial Nonlinear Process Fault Detection Based on Neighborhood Preserving Embedding

Abstract

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.

Keywords

• neighborhood preserving embedding (npe)
• principal polynomial analysis (ppa)
• nonlinear process
• fault detection