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Non-linearity process failure diagnosis method

A fault diagnosis, nonlinear technology, applied in the direction of electrical testing/monitoring, etc., can solve the problems of input space and feature space conversion, unfavorable process, difficulty in fault diagnosis, etc.

Inactive Publication Date: 2008-04-09
NORTHEASTERN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the problem of applying the KICA method to industrial processes is that the dimension of the formed kernel matrix is ​​the square of the number of samples, which is very unfavorable to the process
The ICA method is used to extract independent elements in a dynamic process, but since the input space and feature space cannot be converted as freely as PCA, it makes fault diagnosis difficult

Method used

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Examples

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example 1

[0154] Example 1. Tennessee Eastman process

[0155] The method proposed by the present invention is applied to the Tennessee-Eastman process simulation data and compared with the monitoring results of KPCA and the original KICA. The Tennessee-Eastman process is a complex non-linear process, which was created by Eastman Chemical Company, whose purpose is to provide a real industrial process for evaluating process control and monitoring methods. The control structure is shown in Figure 2. The process includes five main units: reactor, condenser, compressor, separator, and stripper; moreover, it contains eight components: A, B, C, D, E, F, G, and H. Four reactants A, C, D and E are added to the reactor together with inert B to form products G and H, as well as by-product F. The Tennessee-Eastman process includes 21 preset faults, as shown in Table 1. Including 22 continuous process measurements, 12 control variables, and 19 component measurements. As shown in table 2. In addition to...

example 2

[0192] Example 2: Wastewater treatment process (WWTP)

[0193] The monitoring method based on the improved KICA is applied to the wastewater treatment process. It includes activated sludge model No.1 (ASMl) and a ten-layer sedimentation tank model (settler model). Activated sludge model No.1 and a ten-layer sedimentation tank model are used to simulate biological reactions and sedimentation processes, respectively. The process layout of the WWTP system is shown in Figure 7. The first and second compartments of the bioreactor are not inflated, the others are inflated. All compartments are considered to be ideally mixed, while the secondary sedimentation tank is modeled with a series of one-dimensional ten-layers. For process monitoring, eight variables are selected as listed in Table 9, because they are all important and are typical in actual WWTP system monitoring. The variables of this kind of process often fluctuate greatly in a cycle, and their mean and variance cannot always b...

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Abstract

The invention relates to a nonlinear process fault diagnosis method, comprising the steps such as data acquisition, similar analysis, whitening process to the data by utilizing kernel principal component analysis, solving of observation variable z after whitening, independent component exaction by modifying an ICA analysis, and fault detection and diagnosis analysis by utilization of T<2> and SPE statistics and LS-SVM. The invention puts forward the nonlinear dynamic process fault diagnosis technique, combines the advantages of Kernel, ICA and LS-SVM, that is, the exerts the kernel-to-nonlinear express ability, and at the same plays the master ability of ICA to the dynamic characteristic as well as LS - SVM classification ability.

Description

Technical field [0001] The invention belongs to the technical field of fault detection and diagnosis, and particularly relates to a non-linear process diagnosis method based on the combination of an improved kernel independent element analysis and a support vector machine. Background technique [0002] With the rapid development of computer and electronic measurement technology, most modern industrial processes have complete sensing and measurement devices, and a large amount of process data can be obtained online. Statistical analysis of these data can help operators discover process failures in time and avoid major accidents, which promotes people to study process monitoring methods based on data analysis. At present, principal component analysis (PCA), partial least squares (PLS) and independent component analysis (ICA) are the most commonly used methods of this type. However, these methods are all statistical methods based on linear transformation, that is, assuming that the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
Inventor 张颖伟秦泗钊王滢
Owner NORTHEASTERN UNIV
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