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

A nonlinear principal component and nonlinear dynamic technology, which is applied in the field of nonlinear dynamic process monitoring based on explicit polynomial mapping, can solve problems such as redundancy and low efficiency of infinite-dimensional nonlinear mapping, and achieve high fault detection rate and low False alarm rate and impact reduction effect

Active Publication Date: 2019-01-04
保控(南通)物联科技有限公司
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However, infinite-dimensional nonlinear mappings based...

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

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Embodiment Construction

[0043] The technical solutions of the present invention will be further described in detail below in conjunction with specific embodiments.

[0044] figure 1 It is a schematic diagram of the principle of the monitoring method of the present invention. The proposed nonlinear dynamic process monitoring method mainly includes three stages. Stage 1, use CVA to reduce the influence of data dynamic characteristics; stage 2, map the state vector to high-dimensional feature space by displaying polynomial mapping; stage 3, use PCA to determine the first k pivots and remaining residuals, and calculate T 2 and Q c Statistics.

[0045] 1) Dynamic data preprocessing

[0046] Canonical variable analysis is a linear dimensionality reduction method based on multivariate statistical analysis, where the past observation vector y p,r and the future observation vector y f,r It is composed of the measured values ​​of past and future p sampling moments in the data matrix Y respectively:

[0...

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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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Application Information

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IPC IPC(8): G06F17/16G06K9/62
CPCG06F17/16G06F18/2135
Inventor 商亮亮邱爱兵李俊红陈娟单彪严泽
Owner 保控(南通)物联科技有限公司
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