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Non-linearity process failure diagnosis method

A fault diagnosis and non-linear technology, applied in electrical testing/monitoring, etc., can solve problems such as input space and feature space conversion, difficult fault diagnosis, unfavorable process, etc.

Inactive Publication Date: 2009-12-02
NORTHEASTERN UNIV LIAONING
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  • Summary
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  • Claims
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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 (Tennessee Eastman) process

[0155] The method proposed by the invention is applied to the Tennessee-Eastman process simulation data, and compared with the monitoring results of KPCA and original KICA. The Tennessee-Eastman process is a complex nonlinear process that was created by Eastman Chemical Company to provide a real industrial process for evaluating process control and monitoring methods. Control structures such as figure 2 shown. The process consists of five main units: reactor, condenser, compressor, separator, and stripper; moreover, it contains eight components: A, B, C, D, E, F, G, and H. The four reactants A, C, D and E are fed into the reactor together with the inert B to form the products G and H, as well as the by-product F. The Tennessee-Eastman process includes 21 preset failures, as shown in Table 1. Includes 22 continuous process measurements, 12 control variables, and 19 component measurements. As shown in table ...

example 2

[0190] Example 2. Wastewater Treatment Process (WWTP)

[0191] The monitoring method based on the improved KICA is used in the wastewater treatment process. It includes activated sludge model No.1 (ASM1) and a ten-layer sedimentation tank model (settler model). The 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 as follows: Figure 7 shown. The first two compartments of the bioreactor are not aerated, the others are aerated. All compartments are considered to be ideally mixed, while the secondary settling tanks are modeled with a series of one-dimensional ten layers. For process monitoring, 8 variables are selected as listed in Table 9, because they are all important and typical in actual WWTP system monitoring. The variables of this process often fluctuate greatly in a period, and their mean and variance cannot be kept constant...

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Abstract

A nonlinear process fault diagnosis method, including collecting data, performing similarity analysis, using kernel principal component analysis to whiten the data, solving the whitened observed variable z, using modified independent element analysis ICA to extract independent elements, using T2 and SPE statistics and LS-SVM for fault detection and diagnosis steps. The present invention proposes a non-linear dynamic process fault diagnosis technology, combining the advantages of Kernel, ICA and LS-SVM, that is, giving full play to Kernel's ability to express nonlinearity, and at the same time giving full play to ICA's ability to grasp dynamic characteristics and LS-SVM's classification ability.

Description

technical field [0001] The invention belongs to the technical field of fault detection and diagnosis, in particular to a nonlinear process diagnosis method based on the combination of improved nuclear independent element analysis and support vector machine. Background technique [0002] With the rapid development of computer and electronic measurement technology, most modern industrial processes have complete sensing and measuring 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 the research on process monitoring methods based on data analysis. At present, the most used methods of this type are principal component analysis (PCA), partial least squares (PLS) and independent component analysis (ICA). However, these methods are all statistical methods based on linear transformation, which assume that the process variables satisfy ...

Claims

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Application Information

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