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Linear-Nonlinear Industrial Process Fault Detection Method Based on Linear Evaluation Factor

A technology for evaluating factors and fault detection, applied in instrumentation, design optimization/simulation, calculation, etc., can solve problems such as the inability to accurately describe the linear-nonlinear process information of complex systems, achieve accurate statistical models, improve fault detection rates, and improve The effect of fault detection results

Active Publication Date: 2022-02-18
CHINA UNIV OF PETROLEUM (EAST CHINA)
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  • Application Information

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Problems solved by technology

[0003] Aiming at the problem that the traditional industrial process monitoring method cannot accurately describe the linear-nonlinear process information contained in the complex system, the present invention provides a linear-nonlinear industrial process fault detection method based on linear evaluation factors

Method used

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  • Linear-Nonlinear Industrial Process Fault Detection Method Based on Linear Evaluation Factor
  • Linear-Nonlinear Industrial Process Fault Detection Method Based on Linear Evaluation Factor
  • Linear-Nonlinear Industrial Process Fault Detection Method Based on Linear Evaluation Factor

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Experimental program
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Effect test

Embodiment 1

[0171] Embodiment one: at first, design the numerical system that contains 8 variables, its structure is as follows:

[0172] x 1 =u+e 1

[0173] x 2 =-2x 1 +1+e 2

[0174] x 3 = u 2 -3u+e 3

[0175]

[0176] x 6 =2x 5 +1+e 6

[0177] x 7 =sin(8πu)+e 7

[0178]

[0179] Among them, the data source signal u obeys the uniform distribution U(0, 2), e 1 ~e 8 8 independent noises with zero mean and 0.01 variance. Firstly, 500 sets of normal data are simulated as a training set for historical modeling. In addition, two sets of data containing faults are simulated as the test set, and each set of data contains 500 samples. Fault 1: variable x is given at the 201st moment 5 Add a step fault with an amplitude of 0.3.

[0180] The above-mentioned fault detection method of the present invention (hereinafter referred to as the LEF method) is used to detect the fault of the linear-nonlinear numerical system described in this embodiment. After the fault is detec...

Embodiment 2

[0188] Embodiment 2: Tennessee-Eastman (hereinafter referred to as: TE) process is an experimental platform established by Downs and Vogel of Eastman Chemical Company of the United States according to an actual chemical process, and is now widely used to verify control algorithms and process monitoring pros and cons of the method. see image 3 , the TE process is mainly composed of five units, including reactor, product condenser, gas-liquid separator, cycle compressor and stripper. There are 53 variables in the TE process, including 22 continuous measurement variables, 19 component variables and 12 operational variables. In this embodiment, referring to Table 3, 33 variables in the TE process are selected; referring to Table 4, there are 21 faults in total.

[0189] table 3

[0190] variable label variable description variable label variable description 1 A feed (stream 1) 18 Stripper temperature 2 D feed (stream 2) 19 Stripper flow 3 ...

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Abstract

The invention relates to a linear-nonlinear industrial process fault detection method based on a linear evaluation factor. The steps are: firstly, normalize the training data and test data; secondly, define the linear evaluation factor LEF(x i ,x j ), through the linear evaluation factor LEF(x i ,x j ) to measure the different correlations between variables, and divide the linear block and nonlinear block in the industrial process with the help of linear evaluation factors, which can describe the correlation of local variables in more detail; on this basis, establish PCA models in different sub-blocks , KPCA model, and fuse the information of all blocks, judge whether a fault occurs through the fused statistics, and then improve the fault detection result and increase the fault detection rate.

Description

technical field [0001] The invention belongs to the technical field of complex industrial process fault detection, and relates to a linear-nonlinear mixed industrial process fault detection method based on a linear evaluating factor (LEF for short). Background technique [0002] Due to the increasing scale of modern industrial systems, fault diagnosis of industrial processes has become a key technology to ensure the safe and stable operation of modern industrial systems. With the development of modern computer control technology, a wealth of process data is collected and stored in industrial processes. Therefore, data-driven fault detection methods have gradually become a research hotspot in the field of industrial process monitoring. For online linear industrial process monitoring, the classic method is principal component analysis (PCA) method. Aiming at the problem of industrial process monitoring of nonlinear industrial systems, the researchers further proposed the Ker...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F30/20G06F18/2135G06F18/25
Inventor 邓晓刚邓佳伟王磊曹玉苹
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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