Nonlinear dynamic process monitoring method based on canonical variable nonlinear principal component analysis

Abstract

The invention discloses a non-linear dynamic process monitoring method based on the non-linear principal component analysis of a normalized variable, which comprises the following steps: acquiring a data matrix Y, pre-specifying a value of p and a system order n; the Hankel matrix of the past and future observational side values being combined according to the formula; calculating covariance and cross-variance matrices of past and future observations; singular value decomposition of H matrix; calculating a state vector and a residual vector; the state vector being projected onto the high dimensional feature space by explicit second order polynomial mapping; the first k principal components being determined by eigenvalue decomposition in principal component analysis; finally, the T2 statistic, the combined statistic Qc and their corresponding control limits being calculated. The method of the invention is used for monitoring three different types of faults in the Eastman chemical process of Tennessee, and the simulation results show that the proposed CV-NPCA method has high fault detection rate and relatively low fault false alarm rate.

Description

technical field [0001] The invention relates to a nonlinear dynamic process monitoring method in the field of data-driven technology, in particular to a nonlinear dynamic process monitoring method based on explicit polynomial mapping. Background technique [0002] Traditional multivariate statistical process monitoring methods are limited by the assumptions of linearity and normal distribution of measured variables, such as principal component analysis and canonical variable analysis. When they are used for the monitoring of nonlinear dynamic industrial processes, they will have a high failure rate False positive rate and low failure detection rate. Kernel principal component analysis based on radial basis functions has been applied in many nonlinear industrial processes. However, infinite-dimensional nonlinear mapping based on uncertain kernel functions is inefficient and redundant. Contents of the invention [0003] Purpose of the invention: The purpose of the present ...

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Problems solved by technology

However, infinite-dimensional nonlinear mappings based on uncertain kernel functions are inefficient and redundant

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