Double main element-dynamic kernel principal component analysis fault diagnosis method based on chemical TE process

A dynamic core pivot and fault diagnosis technology, applied in design optimization/simulation, special data processing applications, complex mathematical operations, etc., and can solve problems such as low model accuracy

Active Publication Date: 2018-05-22
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0004] However, the accuracy of the model built for the chemical process is low at present. In this regard, t

Method used

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  • Double main element-dynamic kernel principal component analysis fault diagnosis method based on chemical TE process
  • Double main element-dynamic kernel principal component analysis fault diagnosis method based on chemical TE process
  • Double main element-dynamic kernel principal component analysis fault diagnosis method based on chemical TE process

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Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0085] 1. Generate a dynamic matrix

[0086] Select normal sample data, calculate the mean and standard deviation, and standardize the sample data to construct a training matrix; determine the optimal order and generate a dynamic matrix;

[0087] For example, firstly, the original data (including training samples 480*52 and test samples 960*52) are standardized, and the processing steps are as follows:

[0088] Suppose the original data x of n×p dimension ij , the matrix of observed values ​​after standardized transformation is

[0089]

[0090] in

[0091]

[0092] After standardized transformation, the mean value of each column of matrix X is 0, and the standard deviation is 1.

[0093] The key to solving the autocorrelation problem of the DKPCA model is to determine the order h of the autoregressive model. Generally, h=1 or 2 is used in engineering applications, and the dynamic characteristic determination algorithm (DOD) is used to analyze the dynamic relationship...

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Abstract

The invention discloses a double main element-dynamic kernel principal component analysis (DME-DKPCA) fault diagnosis method based on a chemical TE process. According to the method, a DOD algorithm isused to determine an optimal parameter order of process data, after a generated dynamic matrix projects in a kernel principal element space, the suppression ability of an R principal component method to high-dimensional noise is used to effectively determine the number of principal components of normal state data in a kernel feature space, and T2 and SPE control limits of a fault detection high-dimensional space are generated; then a CPV method is used to retains fault information to the maximum extent, the number of new principle components is determined from data to be detected, and T2 and SPE statistics are recalculated to monitor and detect a fault. According to DME-DKPCA method, the recognition rate of fault detection is effectively improved, and the method has better model accuracy than a DKPCA method.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a double principal component-dynamic core principal component analysis fault diagnosis method based on chemical TE process. Background technique [0002] The chemical TE (Tennessee Eastman, Tennessee-Eastman) process is a simulation of an actual chemical process. It was proposed by J.J.Downs and E.F.Vogel of the process control group of Tennessee Eastman Chemical Company in the United States, and is widely used in the research of process control technology. This process model is first provided to the process control academic community in the form of FORTRAN source code, which mainly describes the nonlinear relationship among devices, materials and energy. The TE model can be mainly used for device control scheme design, multivariable control, optimization, model predictive control, nonlinear control, process fault diagnosis, teaching, etc. The research and development of the multi-wo...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/16G06F17/18G06F17/50
CPCG06F17/16G06F17/18G06F30/20G06F18/213G06F18/214
Inventor 苏盈盈康东帅罗妤
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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